Fuel optimization display

The present invention is an apparatus and method for guiding the driver of a vehicle in selecting an accelerator pedal position and transmission gear to improve a score. The score may be a weighted average of a fuel economy score and a drivability score. A curve showing the best score for each gear may be shown on a display as a function of accelerator pedal and transmission gear number, along with the current accelerator pedal and gear number of the vehicle. The driver may improve the score by changing to an accelerator pedal and/or gear selection that is closer to the curve. The curve may be calculated, using a model based on forces and torques upon the vehicle, from data acquired by monitoring the vehicle and/or from external sources.

FIELD OF THE INVENTION

The present invention relates to a fuel optimization in vehicles. More specifically, the present invention relates to a user interface to guide a driver in selecting accelerator pedal position and gear.

BACKGROUND OF THE INVENTION

Improving fuel efficiency in heavy-duty vehicles provides numerous benefits to the national and global communities. Heavy-duty vehicles consume a substantial amount of diesel fuel and gasoline, increasing dependence on fossil fuels. In the United States, medium and heavy-duty vehicles constitute the second largest contributor within the transportation sector to oil consumption. “EPA and NHTSA Adopt First-Ever Program to Reduce Greenhouse Gas Emissions and Improve Fuel Efficiency of Medium- and Heavy-Duty Vehicles”, Regulatory Announcement EPA-420-F-11-031, U.S. Environmental Protection Agency, August 2011 (hereinafter, “EPA Fact Sheet”). Currently, heavy-duty vehicles account for 17% of transportation oil use. “Annual Energy Outlook 2010”, U.S. Energy Information Admin., Report DOE/EIA-0382 (2010), April 2010. Demand for heavy-duty vehicles is expected to increase 37% between 2008 and 2035 (EPA Fact Sheet), making the need for more fuel-efficient vehicles even more apparent.

Heavy-duty vehicles also emit into the atmosphere carbon dioxide, particulates, and other by-products of burning fossil fuels. The EPA estimates that the transportation sector emitted 29% of all U.S. greenhouse gases in 2007 and has been the fastest growing source of U.S. greenhouse gas emissions since 1990. “Inventory of US Greenhouse Gas Emissions and Sinks: 1990-2009”, Report EPA 430-R-11-005, Apr. 15, 2011. By improving fuel efficiency in heavy-duty vehicles used in the U.S., the amount of greenhouse gases emitted could be drastically reduced. The benefits of improved fuel efficiency have prompted the Obama Administration to implement new regulations mandating stricter fuel efficiency standards for heavy-duty vehicles. In August 2011, the Environmental Protection Agency and the Department of Transportation's National Highway Traffic Safety Administration released the details of the Heavy Duty National Program, designed to reduce greenhouse gas emissions and improve fuel efficiency of heavy-duty trucks and buses. The Program will set forth requirements for fuel efficiency and emissions from heavy-duty vehicles between 2014 and 2018 in a first phase, and from 2018 and beyond in a second phase. The key initiatives targeted by this program are to reduce fuel consumption and thereby improve energy security, increase fuel savings, and reduce greenhouse gas emissions (EPA Fact Sheet). Creating sustainable processes for improving fuel efficiency of heavy-duty vehicles would allow vehicle owners to comply with the new emission standards, and would further the initiatives of the Heavy Duty National Program.

Poor fuel economy consumes resources that a vehicle operator might more profitably spend on opportunities that also benefit the economy as a whole. The EPA and Department of Transportation have estimated that the Heavy Duty National Program would result in savings of $35 billion in net benefits to truckers, or $41 billion total when societal benefits, such as reduced health care costs because of improved air quality, are taken into account. EPA Fact Sheet.

SUMMARY OF THE INVENTION

In the context of commercial vehicle fleets, a trip or mission often requires that a particular payload is moved from a point A to a point B at a particular time. The amount of fuel used for a mission will be affected by the particular choice of vehicle, by the geography (e.g., topography), by speed limits and other regulations, by traffic, and by the habits of the particular driver in operating the vehicle. Due to any or all of these factors, any mission can be expected to use more fuel than is optimal. Some of these factors, such as the choice of vehicle and how the driver operates the vehicle, can be manipulated, while others, such as regulations and traffic on a given route, cannot.

The inventor expects that the driver is often a major source of vehicle performance inefficiency. However, until now there has not been sufficient data to assess the magnitude of that inefficiency, an information gap that the data collection and analysis methodology of the invention will help to fill. Another goal of the invention is improving driver performance. By modeling vehicle dynamics and collecting and storing relevant data, factors subject to control of a driver or a fleet manager may be optimized.

Actual performance of a driver may be measured by one or more scoring functions. A scoring function may be based on indicia with regard to a “goodness” factor. For example, the fuel efficiency and the drivability of the vehicle are candidates for goodness factors that might each be rated by a respective scoring function. A given scoring function may be a composite of other scoring functions. Thus, an overall score might be a composite of a fuel efficiency score and a drivability score. A composite function may weight such scoring functions for individual goodness factors. The weighting may be constant, or might itself be a function of state of the vehicle. For example, acceleration (more specifically, positive acceleration) may be a factor in drivability, but the driver's need to accelerate is less at higher speeds. The overall scoring function might weight the vehicle's ability to accelerate more heavily, relative to fuel consumption, at slower speeds than at higher speeds.

The reserve or available acceleration is the acceleration that the vehicle would have at the current speed if the vehicle were given full throttle; in other words, the accelerator pedal is 100 percent depressed. Because reserve acceleration may be more important to drivability than actual acceleration, reserve acceleration may be preferable as a goodness factor in scoring. Whether reserve acceleration or actual acceleration is intended will be distinguished in particular contexts in this document.

A scoring function, for a goodness factor such as fuel efficiency, might involve a comparison of a measured value with, or ratio to, one or more reference values. A reference value for fuel efficiency might be, for example, (1) the best fuel efficiency ever measured for this particular vehicle; (2) the average fuel efficiency recorded by drivers in a fleet for this model of vehicle; (3) a government or manufacturer estimate of average fuel efficiency for this model of vehicle; (4) the best fuel efficiency achieved by any vehicle available from any manufacturer within this class of vehicles; or (5) a target fuel efficiency, possibly set by an expected future regulation or by a company's goals.

When operating a vehicle, driver manipulates certain vehicle “controls”, such as a gear stick to control transmission gear, an accelerator pedal (or throttle pedal) to control fuel usage, and brake pedal to slow the vehicle. We may sometimes use “accelerator” or “throttle” as short for accelerator/throttle pedal; “gear” as short for “transmission gear stick”; and “brake” as short for brake pedal. If the vehicle has a manual transmission, the driver also controls the clutch position in order to shift gears. Because braking is dictated primarily by regulations and traffic, a driver's choices with respect to braking are unlikely to be much improved upon. Nor is it practical to change a driver's habits regarding the use of clutch and gear shift stick in moving from one gear to the next.

Drivability and fuel economy are dependent on accelerator position and transmission gear, and with regard to those particular vehicle controls, the driver usually has some choices. Consider exemplary individual scoring functions for drivability and fuel economy, and an overall scoring function that is a weighted average of them. At any given time while a vehicle is being driven, and for any given choice of transmission gear, there is expected to be an accelerator position that optimizes the overall scoring function (as well as accelerator positions that optimize the individual scores for the component factors). Thus, taken together, the optimal (with respect to the overall scoring function) gear-accelerator pair choices form a curve to which the driver may aspire. Each gear-accelerator optimal pair is associated with an efficiency score, a drivability score, and an overall score. One of the gear positions will have a highest overall score.

Depending on the formulation of the overall scoring function, the various scores, and hence the curve, may either be static for a particular mission, or change over time. For example, if weightings of component scores change with vehicle speed, then the shape of the curve may change frequently or even constantly. Environmental factors may also cause the curve to evolve, such as road rolling resistance, aerodynamic drag due to wind changes, road grade, temperature, elevation, rain or snow, and ice.

Indicia of driver performance include current values of variables relating to fuel-efficiency. By “current” we mean averaged over a short period, e.g., over an interval of 10 seconds or some shorter period. By “instantaneous” or “near real time” we mean a time no more than 1 second. Variables may include some or all of the following: current gear and accelerator control positions; the actual drivability, fuel-efficiency, and overall scores that the vehicle is presently achieving under control the driver; the optimal gear-accelerator pairs and their scores; and the evolving aspirational curve. The indicia may also include indicia spanning longer times than “current”, such as values averaged or integrated since the start of the mission. These may include, for example, average fuel consumption rate, total fuel used, total miles driven, and average values of various goodness scores.

Such indicia of driver performance may be shown through a user interface (UI) on a monitor or display. The vehicle may be equipped with such a UI to influence the driver's operation of the vehicle. A chart may display the current grid-accelerator pair and a curve of optimal grid-accelerator pairs, and include respective representations of scores for these various pairs. A driver, or a group of drivers, might be recognized for meeting or exceeding threshold values of one or more of the indicia during a single mission, or averaged over a set of missions in an awards program sponsored by a fleet manager.

Such indicia of driver performance may be collected in tangible electronic storage (e.g., memory, flash drive, solid state disk, rotational media drive). Such storage may be located on the vehicle itself, at some remote location, or some combination thereof. Data about the vehicle design, the state of the vehicle and its components (including, for example, driver controls, fuel consumption, powertrain state, payload, and environmental conditions) may also be saved to such storage. Data may be collected from various sources including, for example: a controller-area network (CAN) on the vehicle; other sensors on the vehicle, such as a global position system (GPS) sensor; environmental sensors on the vehicle; external sources such as weather stations; and manufacturers' specifications for the vehicle or its components. Physical dynamics models may calculate unknown parameters from such data, and use the results as feedback to guide a driver.

A trip dynamics “executor” (TDE) may collect data from a vehicle and external sources, analyze that data, and initiate appropriate actions, for example, to provide diagnostics to a driver. The TDE may include a logger to collect relevant data, a kernel for to analyze information and control execution, and a monitor to provide diagnostics to a user. These elements may include or utilize sensors, logic executed by processing hardware, and communications systems. The logic may include hardware logic, software logic based on instructions accessed from storage and executed by hardware, or any combination thereof. Data collection may use a device that connects to a CAN connector, such as a J1939 connector, on a vehicle. Sensors may be located, and the logic may be executed, by hardware on the vehicle and/or at one or more remote location. When some or all of the hardware for the logic, or the storage or sources for the data, is remote, then the one or more communication systems may be used to communicate relevant information as required. By the term “communication system”, we mean any system capable of transmitting and/or receiving information electronically; for example, alone or in combination, whether wired or wireless: a local area network (LAN), a wide area network (WAN), a personal area network (PAN), a hardware bus, or a cable.

Indicia of driver performance collected by one or more individual vehicles may be received over a communication system at some remote facility for display or analysis. Indicia might be averaged over a set of vehicles, and/or over some interval of time. A manufacturer might use such data to evaluate its vehicles or the vehicles of a competitor. A fleet operator might use such data for accountability of its drivers, or to make decisions about current environmental conditions.

Reserve acceleration (and hence drivability) depends on vehicle physical dynamics processes, and, in particular, on the net force applied to the vehicle. The net force on the vehicle depends on the vehicle load, environmental conditions, and fuel usage. Fuel usage, in turn, depends on the driver's operation of the gear and accelerator controls. Current fuel usage can be monitored, although accuracy may require some function fitting or estimation based on observation of the current state of the internal components of the vehicle. Fuel drives the engine, which produces torque. The torque is transmitted, albeit with some loss to heat and vibration, through the powertrain (e.g., clutch or torque converter; transmission; and rear axle), to the wheels and tires. Force on the vehicle due to fuel usage depends on torque, generated from fuel consumption, on the tires.

The logic combines a trip dynamics model of vehicle components and such physical dynamics processes, real-time observations about the vehicle and the environment, and data known about the vehicle from the manufacturer or previous data collection and analysis. The model uses mathematical and physical equations, which may be approximated (e.g., discretized or otherwise simplified), to calculate or estimate indicia of driver performance. Any or all of the data used in these calculations, as well as the results of the calculations, may be saved to and/or retrieved from tangible storage.

An exemplary model will be presented in the Detailed Description of this document. Each item contained in the display is a variable in the model, and those variables are organized herein into a set of variable tables, each table containing a group of variables that are related to a vehicle system or to a component of the TDE (e.g., the display). There are also a set of equation tables, each table containing a set of equations similarly grouped. Each variable table also gives one or more sources for how a variable may be obtained. A source is either a basic source—a generally known quantity (e.g., gravitational acceleration), a measurement or observation (e.g., engine speed, road grade), a specification provided by a manufacturer, a statistic based on historical observation of vehicles, or a user preference—or an equation in the equation tables. When the source is an equation, the variable will be related functionally to other variables in the variable tables. Each of these other variables can therefore be sourced analogously. All variables in the display, and indeed all variables in the particular model provided herein as exemplary, can be traced by the above process back to a set of basic sources. The tables, therefore, provide a complete (in an exemplary embodiment of the invention) set of processes for obtaining any variable in the exemplary model and in any of the figures.

In addition to coaching a real driver in a real vehicle, other applications of the trip dynamics model, and observations collected by TDEs in one or more vehicles, are possible within the scope of the invention. For example, (1) a real driver might be taught how to improve fuel efficiency with a simulated vehicle that displays indicia of driver performance; (2) a fleet manager might evaluate a particular vehicle by simulating a set of typical missions for that fleet with the vehicle to see how it compares with other vehicles; or (3) a manufacturer of a vehicle, or of a vehicle component, might evaluate various candidate configurations of design to predict performance and choose a best design.

The modeling approach has much wider applicability than the trip dynamics display. Suppose, by way of illustration, that an equation specifies A as a function of B and C, and suppose that function is not known publicly. For example, a vehicle or component manufacturer might know the function, but might not be willing to reveal it for competitive or legal reasons. Using the vast amounts of data that can be collected by the TDEs from operation of real vehicles and from sources of environmental data, mathematical fitting of the equations of the model can be used to infer such relationships quite accurately.

The equations in the model may be used in different sequences for different purposes. If the source of variable B in the source tables is an equation that shows B to be a function of A, then A is also mathematically related to B, but A might not be a function of B. For a given value of B, there may be more than one value of A. In such a case, data collection can be used to eliminate the ambiguities, allowing such a relationship to find the correct value of A in particular situations.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

This description provides embodiments intended as exemplary applications of the invention. The reader of ordinary skill in the art will realize that the invention has broader scope than the particular examples described here. Although many of the concepts and innovations apply to any motor vehicle, the primary area of applicability of teachings herein is heavy-duty vehicles, especially commercial trucks.

FIG. 1-10are tables that define a set of exemplary variables which pertain to the dynamics of a heavy-duty vehicle. Each figure contains a set of variables, in table rows, loosely grouped by system or by function. The groupings provide a convenient but rather arbitrary organization, and other groupings may be equally useful. Many of the variables will be used in subsequent figures and associated text. The variables are abbreviated by symbols, many of them involving subscripts, superscripts, and Greek letters. The table organization of the variables and equations will hopefully simplify reading and understanding this document for the reader. The reader will recognize that the variables and equations tables represent illustrative embodiments of the invention. Other embodiments may use some additional variables or equations, or some different variables or equations, or fewer variables or equations.

All of the variables tables have the same column headings, so only the column headings in the first variables table have been given reference numerals. The first column in each variables table is reference numeral (REF.130). The second column is the symbol (SYM.131) for the variable. The third column is a definition of the variable. The next four columns (columns 4-7) give a source or sources for the variable in the model. A variable may have one or more source, and not all possible sources are listed in the tables. A variable may be measured (MEAS.133), obtained from an equation (EQN.134), specified (SPEC.135), or simply a quantity or function that may vary (VBL.136), such as time or throttle pedal position. The MEAS.133column contains the following entries: CAN (a network on a vehicle); History (statistics from previously collected data); ECU (a controller in a vehicle); GPS (a locating device); internet sources (WWW); or Scale (to measure weight). The EQN.134column refers to an equation, by equation number in the equations table, from which the variable may be calculated. Sources in the SPEC.135column are means of specification. These include “User” for user-specified; “Mfr.” for a value specified by a vehicle or component manufacturer; “Mfr map” for a mapping, table, or function from a manufacturer; “Tire mfr. map” for such a map, specifically from a tire manufacturer; or “Const.” for a known constant. The VBL.136is checked with an “x” for variable quantities. The USED137column lists numbers for equations in which the particular variable appears.

FIG. 1defines the following variables and corresponding symbols related to driver performance scoring: current throttle pedal position101; current clutch pedal position102; current transmission gear number103; fuel economy score104; time-averaged fuel economy score105; fuel economy weight factor106; instantaneous drivability107; average drivability108; maximum drivability109; drivability score110; time-averaged drivability score111; drivability weight factor112; score113; current score114; score function115; best score116; best score for any gear117; throttle step size for the grid118; throttle position119; best throttle position120; best gear number121; best throttle position122; and time-averaged score123.

FIG. 2defines the following variables and corresponding symbols related to vehicle motion: vehicle velocity201; vehicle speed202; distance traveled203; vehicle acceleration204; magnitude of vehicle acceleration205; vehicle position206; magnitude of reserved vehicle acceleration207; mass of payload208; mass of chassis209; mass of body214; mass of trailer215; vehicle mass210; effective vehicle mass211; time212; and particular time213.

FIG. 3defines the following variables and corresponding symbols related to the engine and fuel system: trip fuel301; fuel mass flow rate302; instantaneous fuel economy at steady state303; average fuel economy304; maximum fuel economy305; angular speed306; angular acceleration307; engine idle angular speed308; engine governed angular speed309; engine moment of inertia310; engine indicated torque311; engine friction torque312; engine brake torque313; engine load torque314; and engine effective torque315.

FIG. 4defines the following variables and corresponding symbols related to the clutch on a vehicle having a manual transmission: clutch pedal position401; clutch input speed402; clutch output speed403; clutch speed difference404; Maximum clutch speed difference405; clutch input torque406; clutch output torque407; clutch maximum friction torque408; and parameters409; and410.

FIG. 5defines the following variables and corresponding symbols related to the torque converter (TC) on a vehicle having an automatic transmission: TC angular input (pump) speed501; TC angular output (turbine) speed502; TC input torque503; TC output torque504; TC speed ratio505; TC efficiency ratio506; and TC power ratio507.

FIG. 6defines the following variables and corresponding symbols related to the transmission: transmission gear numbers601; transmission gear ratio602; current transmission gear ratio603; forward transmission gears604; reverse transmission gears605; transmission input speed606; transmission output speed607; transmission gear efficiency608; transmission input torque609; transmission output torque610; and transmission moment of inertia611.

FIG. 7defines the following variables and corresponding symbols related to the rear axle: rear axle input speed701; rear axle output speed702; rear axle gears703; rear axle current gear ratio704; gear efficiency at gear ratio705; rear axle input torque706; rear axle output torque707; and rear axle moment of inertia708.

FIG. 8defines the following variables and corresponding symbols related to the rear axle tires and wheels: tractive torque801; tractive force802; effective combined gear ratio803; driveline efficiency804; Wheel angular speed805; Wheel angular acceleration806; moment of inertia807; Effective moment of inertia808; tire radius809; tire pressure810; and tire temperature811.

FIG. 9defines the following variables and corresponding symbols related to the brakes: brake pedal position901; current brake pedal position902; and brake force903.

FIG. 10defines the following variables and corresponding symbols related to resistive forces acting on the vehicle: elevation1001; air pressure1002; air temperature1003; air density1004; wind velocity1005; effective area1006; aerodynamic drag coefficient1007; grade angle1008; longitudinal gravitational force1009; normal gravitational force1010; gravitational acceleration1011; aerodynamic drag1012; rolling resistance coefficient1013; rolling resistance force1014; and resistive force1015.

These variables are related to each other in the exemplary system of model equations shown in the equations tables: driver performance scoring (FIG. 19); vehicle motion (FIG. 20); fuel consumption and engine dynamics (FIG. 21); clutch dynamics (FIG. 22); torque converter dynamics (FIG. 23); transmission dynamics (FIG. 24); rear axle dynamics (FIG. 25); tire and driveline dynamics (FIG. 26); brake dynamics (FIG. 27); and dynamics of resistance to vehicle motion (FIG. 28). The columns in each of these equations tables are EQUATION1920(the equation) and NUM.1921(the equation number).

FIG. 11illustrates an exemplary display1100in a trip dynamics executor (TDE)1360, which may guide a driver1350in selecting a transmission gear number601and a throttle position119to optimize fuel economy. The display1100depicts a user interface (UI)1130that includes a chart1101and a set of performance statistics1120or diagnostics1120. The chart1101may include a grid1140. The grid1140includes a horizontal axis that represents transmission gear number601and a vertical axis that represents throttle position119. At any given time, the current throttle pedal position101and current transmission gear number103chosen by the driver1350may be indicated on the grid1140as a point, at the center of a square, representing the current gear-throttle pair1102.

For every transmission gear number601, there may be a best throttle position120, which is “best” objectively because it maximizes (or minimizes) some user-selected score function115. The resulting score is the best score116for that transmission gear. The pair of a transmission gear number601and the best throttle position120for that gear describe a point1106on the grid1140. The set of all such best points1106lie on a curve1103, and may be indicated by circles in the display. As illustrated, the diameter1105of each such circle is proportional to the score113for that point1106. Similarly, the size of the symbol (in this case, a square) for the current gear-throttle pair1102is correspondingly proportional to its score113. The pair of best gear number121and best throttle position120correspond to the point best grid-throttle pair1104on the curve1103having the highest overall best score for any gear117is emphasized, in this example by shading. Other means of emphasis might be used, such as color, crosshatching, or animation. For esthetic reasons, a dashed line is shown passing through the circled points on the curve1103, although obviously transmission gear numbers have only integer values.

Note that there are many other ways that regions of relatively good or bad scores113on the grid might be displayed. One such method would be a color contour plot of the scoring function, which can be regarded as describing a surface above the grid1140. The invention encompasses all approaches of representing scoring information to the driver1350for guidance.

The driver1350might improve the performance score113by adjusting the throttle position119and/or shifting to a different transmission gear number601to move to a point on the grid1140where the goodness113is higher. For example, by simply shifting from 3rd to 6th or 7th gear, performance will be improved. Ideally, the driver1350in the illustrated situation would be in 9th gear and have the throttle 83% depressed.

One might ask why the grid1140shows any points on the curve1103other than the best grid-throttle pair1104. We note in response that ambient traffic and regulatory conditions might preclude the driver1350from operating the vehicle1300at the best point. Consequently, the driver1350needs more information than the best grid-throttle pair1104to optimize performance under such constraints. A more sophisticated scoring system in an embodiment of the invention might take such constraints imposed upon the driver1350into account in more fairly rating performance. A constraint might be known (e.g., a speed limit or a construction zone) or inferred (e.g., the vehicle1300is determined based upon observations by the trip dynamics logger1361) to be moving slower than posted speeds on a highway segment known for stop-and-go rush hour traffic). Real time traffic data from external sources might also be taken into account. The scope of the invention includes any scoring system that utilizes a model of vehicle dynamics to estimate driver performance scoring parameters and, hence, includes such more sophisticated systems.

The performance statistics1120fall into two categories, trip diagnostics1121and current diagnostics1122. The current diagnostics1122include current values of fuel economy score104; drivability score110; and overall score113; and instantaneous fuel economy at steady state303. The trip diagnostics1121include time-averaged (typically, over a trip or mission) values: time-averaged fuel economy score105; time-averaged drivability score111; and overall time-averaged score123; and average fuel economy304, as well as total distance traveled203and trip fuel301. A fleet manager might provide a driver with an incentive or reward for achieving a score (whether fuel, drivability, or overall) in some specified range.

A purpose of the chart1101and diagnostics1120in some embodiments of the invention is to improve performance by the driver1350of a vehicle1300. As shown inFIG. 13, the driver controls1310that are relevant to the TDE1360include clutch pedal1313, throttle1311, gear stick1312, and brake pedal1314.FIG. 12is a driver time series chart1200illustrating how those driver controls1310might be manipulated over some interval of time212to shift gears. The graphs for throttle position119, clutch pedal position401, transmission gear number601, and brake pedal position901are synchronized with a common time axis1201. The graphs show, respectively, current throttle pedal position101, current clutch pedal position102, current transmission gear number103, and current brake pedal position902.

As shown byFIG. 12, a sequence of driver events1250occur during the time interval. This current information is typical of the kind of dynamic information that can be observed by the trip dynamics logger1361and analyzed by the trip dynamics kernel1362. The driver starts disengaging the current gear1251, then fully depresses the clutch1252, then shifts to the new gear1253, then starts engaging the new gear1254, and finally fully engages the new gear1255. The brake pedal1314is not used during this sequence. As shown in the tables ofFIGS. 1 and 9and the vehicle model ofFIG. 13, driver events1250are available through a communication network within the vehicle1300to the TDE1360for storage, analysis, and to provide diagnostics to users. Most modern heavy-duty vehicles are equipped with a CAN1380communication system, which may be accessible through a connector in the vehicle1300, usually a J1939 connector in the dashboard.

As mentioned previously, a driver1350might be a simulated or virtual driver rather than a human. Collection of data by a TDE over time will allow drivers1350of various types (e.g., having a specified number of years of experience; employed by a particular fleet manager; or assigned certain metropolitan areas) to be simulated with statistical accuracy. A typical statistical distribution of such driver1350types might be used to evaluate how a vehicle1300or a fleet might perform over a suite of varying conditions (e.g., load, distance, environment). When optimizing a score function or other reference function, we are in effect operating the vehicle1300with a virtual driver1350, using our models to determine choices to test various combinations of choices or actions by such a virtual driver1350result in the optimum set of choices. A virtual vehicle1300might be used to compare various choices of vehicles to determine which vehicle, or suite of vehicles, is optimal for a particular task or suite of tasks.

FIG. 13is a model of a system including a vehicle1300, a driver1350, and an external environment1351. As described in the legend1390, illustrative physiological1391, physical/information1392, and torque1393inputs are indicated by arrowhead type. The model is one instance of a class of models, within the scope of the invention, whereby physiological inputs from the driver modify the motion of a vehicle through transfer of physical quantities.

Physiological1391inputs from the driver1350is transferred to the engine control unit (ECU, also known as the power-train control module)1321over the CAN1380, as indicated by arrow1383, to set the fuel mass flow rate302to the engine1322. Information about the state of systems in the vehicle1300, such as engine angular speed306and engine brake torque313, are transferred to the ECU1321, and may be accessed by the TDE1360over the CAN1380, as indicated by arrow1381.

Resulting engine brake torque313is transferred to the engine-to-transmission coupling1323(a clutch for a manual transmission1331or a torque converter for an automatic). The output torque from the coupling1323is transferred to the driveline1330(including the transmission1331, the drive shafts1332, and the rear axle1333) as transmission input torque609. Output torque from the driveline1330is transferred to the rear wheels and the rear tires1340as rear axle output torque707.

Information about the environment1351in which the vehicle1300is operating is transferred over the CAN1380to the vehicle1300, as indicated by arrow1382. Such environmental data may be available to the TDE1360over the CAN1380as well.

Environmental conditions1371and the payload1341exert a load torque1342on the rear tires1340. The combined torque on the rear tires1340results in a tractive force802on the vehicle1300, causing it to accelerate. The reserve acceleration is calculated by assuming the application of full throttle starting from a vehicle1300moving at steady state in the current transmission gear number103.

Like the driver1350, a vehicle1300may be real or simulated. Simulated vehicles are useful at least for vehicle, system, and component design; driver training; fleet cost estimation; and mission route selection. Likewise, the evolution of an environment1351can be simulated, based on statistics or a dynamic model of the atmosphere, and geographic information systems when convenient for some purpose at hand.

FIG. 13shows an exemplary TDE1360, which includes a trip dynamics logger1361; a trip dynamics kernel1362; and a trip dynamics display1100. The trip dynamics logger1361collects, and stores in tangible storage, data accessed from the CAN1380. This data may pertain to any of the components of the vehicle1300, as well as to any other data collected by vehicle systems and sensors, such as environmental data. Environmental and map data may also be collected and stored by the trip dynamics logger1361from other sources (not shown), such as weather stations and Internet websites, research facilities, or company or government databases.

The trip dynamics kernel1362may analyze data, communicate information, and cause actions to be taken. The trip dynamics kernel1362may compute the variables such as those in the tables ofFIG. 1-10, possibly using a vehicle1300model such as that ofFIG. 13, combined with a physical dynamics model such as that illustrated by the equation tables ofFIG. 19-28. The kernel1362may produce and manage a trip dynamics display1100as exemplified byFIG. 11.

Note inFIG. 13that arrow1381is double headed. In some embodiments of the invention, the kernel1362may determine that the vehicle1300itself is operating suboptimally, and send a command to the ECU1321or other component or system, causing the vehicle1300to change its behavior.

Hardware components of a TDE1360may be located in the vehicle1300, or they may be remote from the vehicle1300. The hardware, logic, and functionality may each be split between local and remote. Local hardware may communicate with remote hardware over a communication system of any type capable of electronically transmitting and/or receiving information. Logic may be embodied in hardware, or in software instructions accessible from hardware devices including tangible storage or communication hardware.

FIG. 14is an exemplary TDE1360showing more detail, particularly of an exemplary trip dynamics logger1361. This trip dynamics logger1361can be inserted into a connector in the vehicle1300. Such a connector, such as a J1939 connector1406is fairly standard in modern heavy-duty vehicles1300. The connector1406puts trip dynamics logger1361into communication with the CAN1380. The trip dynamics logger1361includes a microprocessor1400to execute logic and access data; firmware1401to store instructions and data; a GPS1402device to locate the vehicle1300in three-space—note that another trip dynamics logger1361might include other environmental sensors; tangible storage (removable storage1407in this embodiment) to store instructions and data, and as a form of communication with external devices (by inserting or removing the device); and other forms of communication with the kernel1362, the display1100or with external resources1409—in this example, namely BLUETOOTH1403, Global System for Mobile Communications (GSM)1404, and Wi-Fi1405. The trip dynamics kernel1362and/or logic for the display1100may be running in the microprocessor1400of the trip dynamics logger1361or in some other microprocessor.

FIG. 15illustrates a tree of parameters that may be used to create a chart1101and performance statistics1120likeFIG. 11. Many of these parameters are in the variables tables, or were described in connection withFIG. 11itself. The remaining parameters are user preferences for the chart1101. These include the throttle step1501(i.e., the separation between tick marks on the throttle axis); the symbol1510for the current operation point, as well as its size1511, color1512, and animation1513; and the symbol1520for the best operation point, as well as its size1521, color1522, and animation1523(Color or animation can be used to distinguish certain points on the chart1101in lieu of the shading that was used inFIG. 11.)

FIG. 15illustrates a tree of parameters that may be used to create a chart1101and performance statistics1120likeFIG. 11in near-real-time. Most of these parameters were already described either in the variables tables, or in connection withFIG. 11itself. The remaining parameters are user preferences for the chart1101. These include the throttle step1501(i.e., the separation between tick marks on the throttle axis); the symbol1510for the current operation point, as well as its size1511, color1512, and animation1513; and the symbol1520for the best operation point, as well as its size1521, color1522, and animation1523. (Color or animation can be used to distinguish certain points on the chart1101in lieu of the shading that was used inFIG. 11.)

The trip dynamics kernel1362uses a model of the vehicle1300, such as shown inFIG. 13, to calculate as necessary any of the display components, possibly using data saved by the trip dynamics logger1361.FIG. 16illustrates some of the kinds of processes that may be executed by a trip dynamics kernel1362. The trip dynamics kernel1362may compute engine brake torque1601; compute torque converter output torque1602; compute clutch output torque1603; compute rear axle output speed1604; compute rear axle output torque1605; compute maximum fuel economy1606; compute maximum drivability1607; compute fuel economy1608; compute drivability1609; compute score1610; compute acceleration1611; compute fuel economy score1612; and compute drivability score1613. These processes can be used to populate the UI1130of the trip dynamics display1100and for many other purposes.

A trip dynamics kernel1362that has available a physical dynamics model as illustrated byFIG. 19-28can implement logic to compute a set of variables, such as illustrated byFIG. 1-10. For the particular embodiments described herein, the variables tables and equation tables combine to allow the computation of any “target” variable in the variables tables. Every variable in the variables table has a symbol and at least one source. If a variable has a plurality of sources listed, then any one of those sources is sufficient to obtain the variable. If a source is anything other than an equation (specified in the variables table by equation number), then the source is a base source. If the desired, or target, variable is a base source, then it can be obtained by the trip dynamics kernel1362from that “base” source. Otherwise, the target variable depends on other source variables, as specified in the relevant equation in the equation tables. Such a source variable may itself be a base source, or obtained by some equation in the equation tables; and so forth. In essence, any variable in the variables tables can be regarded as the “root” in a tree diagram, with the base sources as “leaf nodes”.

Once the required data is obtained from the base sources, the relevant equations, which have already been identified in traversing the tree from root to leaf nodes, can be applied to obtain the target variable. In effect, the above discussion demonstrates that all the processes listed inFIG. 16, as well as many more not explicitly listed there, are fully supported in this Description and the drawings.

The above method for obtaining a process whereby any target variable in the variables tables can be sourced or calculated is summarized byFIG. 17. After the start1700, traverse1710backward through the tree of source equations to find the base source variables on which the target variable depends. Obtain1720the values of those base source variables. Apply1730the source equations already found to calculate the target variable from the values of the base source variables. The method ends1730.

The method ofFIG. 17can be used to specify a process to find any variable fromFIG. 15that is included in the variables tables. In effect,FIG. 17is a metaprocess that teaches processes for computing every variable in an embodiment of the dynamics model.

FIG. 18illustrates the method ofFIG. 17for the overall goodness score113variable used in the chart1101ofFIG. 11.FIG. 18illustrates relationships among the variables ofFIG. 1-FIG.10, the equations ofFIG. 19-28, and the processes of the trip dynamics kernel1362shown inFIG. 16. (Note, however, that embodiments may differ with respect to equations, variables, and sources of particular variables.)

InFIG. 18, variables in the vehicle dynamics model, such as score113and average fuel economy304are represented by rectangles. In a given embodiment of the model, a variable is either a base source variable or calculated using an equation from other variables. For example, fuel economy weight factor106, maximum drivability109, and maximum fuel economy305are base source variables, derived from the respective sources user preference1801, historical statistics1802, and manufacturer specification1803, which are shown in rounded rectangles. The users, or stakeholders, that might specify or influence user preferences1801include, for example, the driver1350, a fleet owner/operator, a manufacturer, a supplier, a vehicle designer, a governmental entity, and an organization (e.g., environmental, energy, political).

If a variable is not a base source variable, it may be computed from an equation. Equation numbers that correspond toFIG. 19-28are shown parenthetically inFIG. 18. For example, score113is computed from equation (5). As shown inFIG. 16, the trip dynamics kernel1362may compute score1610as one of its functions, andFIG. 18shows that equation (5) indicates a process for doing so.

Accordingly, score113(in this particular embodiment) is found in equation (5) to depend directly on four variables, namely, fuel economy score104, drivability score110, fuel economy weight factor106, and drivability weight factor112. As taught byFIG. 17, we recurse through the tree to find all the base variables. Once the values of the base variables are obtained from their sources, we then go back up through the equations shown in the tree to ultimately calculate the score113.

In fact, recursion through this particular tree may involve nearly all variables and equations in the model. Triangle1810indicates that the process compute fuel economy1608to compute instantaneous fuel economy at steady state303uses equation (23), the tree expansion of which is omitted fromFIG. 18. Similarly, triangle1811indicates that the process compute drivability1609to compute instantaneous drivability107uses equation (2), the tree expansion of which is also omitted. Note, however, thatFIG. 18merely presents in an alternative form relationships that are already defined, comprehensively for this embodiment, byFIGS. 1-10and19-28.

A few closing remarks aboutFIG. 18are in order. As the figure illustrates, the model configuration allows the trip dynamics kernel1362to calculate any of the variables in the tables. We conclude thatFIG. 16lists only a few of the processes that are taught by this Specification for certain embodiments of the invention. Also,FIG. 16includes the process—compute maximum fuel economy1606—while inFIG. 18, maximum fuel economy305is a base source variable obtained from manufacturer specification1803. This illustrates that there may be more than way to obtain some of these variables. Similarly,FIG. 16includes the process—compute maximum drivability1607—while inFIG. 18, maximum drivability109is a base source variable obtained from historical statistics1802, possibly obtained by the trip dynamics logger1361from observation of this or similar vehicles1300.

Throughout this document and claims, the word “or” is used in the inclusive sense unless otherwise specified. Of course, many variations of the above method are possible within the scope of the invention. The present invention is, therefore, not limited to all the above details, as modifications and variations may be made without departing from the intent or scope of the invention. Consequently, the invention should be limited only by the following claims and equivalent constructions.