Abstract:
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.

Description:
CROSS-REFERENCES TO RELATED APPLICATIONS 
       [0001]    This application claims the benefit of U.S. Provisional Application No. 61/524,832, filed Aug. 18, 2011, and entitled “Fuel Optimization Display”. 
     
    
     FIELD OF THE INVENTION 
       [0002]    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 
       [0003]    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. 
         [0004]    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&#39;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. 
         [0005]    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 
       [0006]    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. 
         [0007]    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. 
         [0008]    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&#39;s need to accelerate is less at higher speeds. The overall scoring function might weight the vehicle&#39;s ability to accelerate more heavily, relative to fuel consumption, at slower speeds than at higher speeds. 
         [0009]    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. 
         [0010]    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&#39;s goals. 
         [0011]    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&#39;s choices with respect to braking are unlikely to be much improved upon. Nor is it practical to change a driver&#39;s habits regarding the use of clutch and gear shift stick in moving from one gear to the next. 
         [0012]    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. 
         [0013]    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. 
         [0014]    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. 
         [0015]    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&#39;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. 
         [0016]    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&#39; 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. 
         [0017]    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. 
         [0018]    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. 
         [0019]    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&#39;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. 
         [0020]    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. 
         [0021]    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. 
         [0022]    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. 
         [0023]    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. 
         [0024]    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. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0025]      FIG. 1  is a table of variables relating to scoring driver performance in a trip dynamics model. 
           [0026]      FIG. 2  is a table of variables relating to vehicle motion in a trip dynamics model. 
           [0027]      FIG. 3  is a table of variables relating to fuel consumption and engine dynamics in a trip dynamics model. 
           [0028]      FIG. 4  is a table of variables relating to clutch dynamics in a trip dynamics model. 
           [0029]      FIG. 5  is a table of variables relating to torque converter dynamics in a trip dynamics model. 
           [0030]      FIG. 6  is a table of variables relating to transmission dynamics in a trip dynamics model. 
           [0031]      FIG. 7  is a table of variables relating to rear axle dynamics in a trip dynamics model. 
           [0032]      FIG. 8  is a table of variables relating to tire and driveline dynamics in a trip dynamics model. 
           [0033]      FIG. 9  is a table of variables relating to brake dynamics in a trip dynamics model. 
           [0034]      FIG. 10  is a table of variables relating to dynamics of resistance to vehicle motion in a trip dynamics model. 
           [0035]      FIG. 11  illustrates an exemplary trip dynamics display to guide a driver in selecting transmission gear and throttle position to optimize fuel economy. 
           [0036]      FIG. 12  is a set of synchronized time series illustrating events in driver operation of vehicle controls. 
           [0037]      FIG. 13  is a block diagram, which represents a vehicle, and a trip dynamics executor to observe and analyze vehicle performance and guide a driver to improve performance. 
           [0038]      FIG. 14  is a block diagram showing components of an exemplary trip dynamics logger. 
           [0039]      FIG. 15  is a tree diagram showing features that are displayed in an exemplary trip dynamics display. 
           [0040]      FIG. 16  is a block diagram showing some of the processes that are performed by an exemplary trip dynamics kernel. 
           [0041]      FIG. 17  is a flowchart for a process that can be used to calculate any variable in the variable tables, using the equations in the model equations tables, from base sources (e.g., observations, manufacturer&#39;s specifications, user preferences, and known values). 
           [0042]      FIG. 18  is a tree diagram showing a process for computing goodness scores by an exemplary trip dynamics kernel. 
           [0043]      FIG. 19  is a table of model equations relating to driver performance scoring in a trip dynamics model. 
           [0044]      FIG. 20  is a table of model equations relating to vehicle motion in a trip dynamics model. 
           [0045]      FIG. 21  is a table of model equations relating to fuel consumption and engine dynamics in a trip dynamics model. 
           [0046]      FIG. 22  is a table of model equations relating to clutch dynamics in a trip dynamics model. 
           [0047]      FIG. 23  is a table of model equations relating to torque converter dynamics in a trip dynamics model. 
           [0048]      FIG. 24  is a table of model equations relating to transmission dynamics in a trip dynamics model. 
           [0049]      FIG. 25  is a table of model equations relating to rear axle dynamics in a trip dynamics model. 
           [0050]      FIG. 26  is a table of model equations relating to tire and driveline dynamics in a trip dynamics model. 
           [0051]      FIG. 27  is a table of model equations relating to brake dynamics in a trip dynamics model. 
           [0052]      FIG. 28  is a table of model equations relating to dynamics of resistance to vehicle motion in a trip dynamics model. 
       
    
    
     DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS 
       [0053]    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. 
         [0054]      FIG. 1-10  are 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. 
         [0055]    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.  133  column 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.  134  column refers to an equation, by equation number in the equations table, from which the variable may be calculated. Sources in the SPEC.  135  column 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.  136  is checked with an “x” for variable quantities. The USED  137  column lists numbers for equations in which the particular variable appears. 
         [0056]      FIG. 1  defines the following variables and corresponding symbols related to driver performance scoring: current throttle pedal position  101 ; current clutch pedal position  102 ; current transmission gear number  103 ; fuel economy score  104 ; time-averaged fuel economy score  105 ; fuel economy weight factor  106 ; instantaneous drivability  107 ; average drivability  108 ; maximum drivability  109 ; drivability score  110 ; time-averaged drivability score  111 ; drivability weight factor  112 ; score  113 ; current score  114 ; score function  115 ; best score  116 ; best score for any gear  117 ; throttle step size for the grid  118 ; throttle position  119 ; best throttle position  120 ; best gear number  121 ; best throttle position  122 ; and time-averaged score  123 . 
         [0057]      FIG. 2  defines the following variables and corresponding symbols related to vehicle motion: vehicle velocity  201 ; vehicle speed  202 ; distance traveled  203 ; vehicle acceleration  204 ; magnitude of vehicle acceleration  205 ; vehicle position  206 ; magnitude of reserved vehicle acceleration  207 ; mass of payload  208 ; mass of chassis  209 ; mass of body  214 ; mass of trailer  215 ; vehicle mass  210 ; effective vehicle mass  211 ; time  212 ; and particular time  213 . 
         [0058]      FIG. 3  defines the following variables and corresponding symbols related to the engine and fuel system: trip fuel  301 ; fuel mass flow rate  302 ; instantaneous fuel economy at steady state  303 ; average fuel economy  304 ; maximum fuel economy  305 ; angular speed  306 ; angular acceleration  307 ; engine idle angular speed  308 ; engine governed angular speed  309 ; engine moment of inertia  310 ; engine indicated torque  311 ; engine friction torque  312 ; engine brake torque  313 ; engine load torque  314 ; and engine effective torque  315 . 
         [0059]      FIG. 4  defines the following variables and corresponding symbols related to the clutch on a vehicle having a manual transmission: clutch pedal position  401 ; clutch input speed  402 ; clutch output speed  403 ; clutch speed difference  404 ; Maximum clutch speed difference  405 ; clutch input torque  406 ; clutch output torque  407 ; clutch maximum friction torque  408 ; and parameters  409 ; and  410 . 
         [0060]      FIG. 5  defines the following variables and corresponding symbols related to the torque converter (TC) on a vehicle having an automatic transmission: TC angular input (pump) speed  501 ; TC angular output (turbine) speed  502 ; TC input torque  503 ; TC output torque  504 ; TC speed ratio  505 ; TC efficiency ratio  506 ; and TC power ratio  507 . 
         [0061]      FIG. 6  defines the following variables and corresponding symbols related to the transmission: transmission gear numbers  601 ; transmission gear ratio  602 ; current transmission gear ratio  603 ; forward transmission gears  604 ; reverse transmission gears  605 ; transmission input speed  606 ; transmission output speed  607 ; transmission gear efficiency  608 ; transmission input torque  609 ; transmission output torque  610 ; and transmission moment of inertia  611 . 
         [0062]      FIG. 7  defines the following variables and corresponding symbols related to the rear axle: rear axle input speed  701 ; rear axle output speed  702 ; rear axle gears  703 ; rear axle current gear ratio  704 ; gear efficiency at gear ratio  705 ; rear axle input torque  706 ; rear axle output torque  707 ; and rear axle moment of inertia  708 . 
         [0063]      FIG. 8  defines the following variables and corresponding symbols related to the rear axle tires and wheels: tractive torque  801 ; tractive force  802 ; effective combined gear ratio  803 ; driveline efficiency  804 ; Wheel angular speed  805 ; Wheel angular acceleration  806 ; moment of inertia  807 ; Effective moment of inertia  808 ; tire radius  809 ; tire pressure  810 ; and tire temperature  811 . 
         [0064]      FIG. 9  defines the following variables and corresponding symbols related to the brakes: brake pedal position  901 ; current brake pedal position  902 ; and brake force  903 . 
         [0065]      FIG. 10  defines the following variables and corresponding symbols related to resistive forces acting on the vehicle: elevation  1001 ; air pressure  1002 ; air temperature  1003 ; air density  1004 ; wind velocity  1005 ; effective area  1006 ; aerodynamic drag coefficient  1007 ; grade angle  1008 ; longitudinal gravitational force  1009 ; normal gravitational force  1010 ; gravitational acceleration  1011 ; aerodynamic drag  1012 ; rolling resistance coefficient  1013 ; rolling resistance force  1014 ; and resistive force  1015 . 
         [0066]    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 EQUATION  1920  (the equation) and NUM.  1921  (the equation number). 
         [0067]      FIG. 11  illustrates an exemplary display  1100  in a trip dynamics executor (TDE)  1360 , which may guide a driver  1350  in selecting a transmission gear number  601  and a throttle position  119  to optimize fuel economy. The display  1100  depicts a user interface (UI)  1130  that includes a chart  1101  and a set of performance statistics  1120  or diagnostics  1120 . The chart  1101  may include a grid  1140 . The grid  1140  includes a horizontal axis that represents transmission gear number  601  and a vertical axis that represents throttle position  119 . At any given time, the current throttle pedal position  101  and current transmission gear number  103  chosen by the driver  1350  may be indicated on the grid  1140  as a point, at the center of a square, representing the current gear-throttle pair  1102 . 
         [0068]    For every transmission gear number  601 , there may be a best throttle position  120 , which is “best” objectively because it maximizes (or minimizes) some user-selected score function  115 . The resulting score is the best score  116  for that transmission gear. The pair of a transmission gear number  601  and the best throttle position  120  for that gear describe a point  1106  on the grid  1140 . The set of all such best points  1106  lie on a curve  1103 , and may be indicated by circles in the display. As illustrated, the diameter  1105  of each such circle is proportional to the score  113  for that point  1106 . Similarly, the size of the symbol (in this case, a square) for the current gear-throttle pair  1102  is correspondingly proportional to its score  113 . The pair of best gear number  121  and best throttle position  120  correspond to the point best grid-throttle pair  1104  on the curve  1103  having the highest overall best score for any gear  117  is 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 curve  1103 , although obviously transmission gear numbers have only integer values. 
         [0069]    Note that there are many other ways that regions of relatively good or bad scores  113  on 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 grid  1140 . The invention encompasses all approaches of representing scoring information to the driver  1350  for guidance. 
         [0070]    The driver  1350  might improve the performance score  113  by adjusting the throttle position  119  and/or shifting to a different transmission gear number  601  to move to a point on the grid  1140  where the goodness  113  is higher. For example, by simply shifting from 3rd to 6th or 7th gear, performance will be improved. Ideally, the driver  1350  in the illustrated situation would be in 9th gear and have the throttle 83% depressed. 
         [0071]    One might ask why the grid  1140  shows any points on the curve  1103  other than the best grid-throttle pair  1104 . We note in response that ambient traffic and regulatory conditions might preclude the driver  1350  from operating the vehicle  1300  at the best point. Consequently, the driver  1350  needs more information than the best grid-throttle pair  1104  to optimize performance under such constraints. A more sophisticated scoring system in an embodiment of the invention might take such constraints imposed upon the driver  1350  into 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 vehicle  1300  is determined based upon observations by the trip dynamics logger  1361 ) 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. 
         [0072]    The performance statistics  1120  fall into two categories, trip diagnostics  1121  and current diagnostics  1122 . The current diagnostics  1122  include current values of fuel economy score  104 ; drivability score  110 ; and overall score  113 ; and instantaneous fuel economy at steady state  303 . The trip diagnostics  1121  include time-averaged (typically, over a trip or mission) values: time-averaged fuel economy score  105 ; time-averaged drivability score  111 ; and overall time-averaged score  123 ; and average fuel economy  304 , as well as total distance traveled  203  and trip fuel  301 . 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. 
         [0073]    A purpose of the chart  1101  and diagnostics  1120  in some embodiments of the invention is to improve performance by the driver  1350  of a vehicle  1300 . As shown in  FIG. 13 , the driver controls  1310  that are relevant to the TDE  1360  include clutch pedal  1313 , throttle  1311 , gear stick  1312 , and brake pedal  1314 .  FIG. 12  is a driver time series chart  1200  illustrating how those driver controls  1310  might be manipulated over some interval of time  212  to shift gears. The graphs for throttle position  119 , clutch pedal position  401 , transmission gear number  601 , and brake pedal position  901  are synchronized with a common time axis  1201 . The graphs show, respectively, current throttle pedal position  101 , current clutch pedal position  102 , current transmission gear number  103 , and current brake pedal position  902 . 
         [0074]    As shown by  FIG. 12 , a sequence of driver events  1250  occur during the time interval. This current information is typical of the kind of dynamic information that can be observed by the trip dynamics logger  1361  and analyzed by the trip dynamics kernel  1362 . The driver starts disengaging the current gear  1251 , then fully depresses the clutch  1252 , then shifts to the new gear  1253 , then starts engaging the new gear  1254 , and finally fully engages the new gear  1255 . The brake pedal  1314  is not used during this sequence. As shown in the tables of  FIGS. 1 and 9  and the vehicle model of  FIG. 13 , driver events  1250  are available through a communication network within the vehicle  1300  to the TDE  1360  for storage, analysis, and to provide diagnostics to users. Most modern heavy-duty vehicles are equipped with a CAN  1380  communication system, which may be accessible through a connector in the vehicle  1300 , usually a J1939 connector in the dashboard. 
         [0075]    As mentioned previously, a driver  1350  might be a simulated or virtual driver rather than a human. Collection of data by a TDE over time will allow drivers  1350  of 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 driver  1350  types might be used to evaluate how a vehicle  1300  or 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 vehicle  1300  with a virtual driver  1350 , using our models to determine choices to test various combinations of choices or actions by such a virtual driver  1350  result in the optimum set of choices. A virtual vehicle  1300  might 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. 
         [0076]      FIG. 13  is a model of a system including a vehicle  1300 , a driver  1350 , and an external environment  1351 . As described in the legend  1390 , illustrative physiological  1391 , physical/information  1392 , and torque  1393  inputs 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. 
         [0077]    Physiological  1391  inputs from the driver  1350  is transferred to the engine control unit (ECU, also known as the power-train control module)  1321  over the CAN  1380 , as indicated by arrow  1383 , to set the fuel mass flow rate  302  to the engine  1322 . Information about the state of systems in the vehicle  1300 , such as engine angular speed  306  and engine brake torque  313 , are transferred to the ECU  1321 , and may be accessed by the TDE  1360  over the CAN  1380 , as indicated by arrow  1381 . 
         [0078]    Resulting engine brake torque  313  is transferred to the engine-to-transmission coupling  1323  (a clutch for a manual transmission  1331  or a torque converter for an automatic). The output torque from the coupling  1323  is transferred to the driveline  1330  (including the transmission  1331 , the drive shafts  1332 , and the rear axle  1333 ) as transmission input torque  609 . Output torque from the driveline  1330  is transferred to the rear wheels and the rear tires  1340  as rear axle output torque  707 . 
         [0079]    Information about the environment  1351  in which the vehicle  1300  is operating is transferred over the CAN  1380  to the vehicle  1300 , as indicated by arrow  1382 . Such environmental data may be available to the TDE  1360  over the CAN  1380  as well. 
         [0080]    Environmental conditions  1371  and the payload  1341  exert a load torque  1342  on the rear tires  1340 . The combined torque on the rear tires  1340  results in a tractive force  802  on the vehicle  1300 , causing it to accelerate. The reserve acceleration is calculated by assuming the application of full throttle starting from a vehicle  1300  moving at steady state in the current transmission gear number  103 . 
         [0081]    Like the driver  1350 , a vehicle  1300  may 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 environment  1351  can be simulated, based on statistics or a dynamic model of the atmosphere, and geographic information systems when convenient for some purpose at hand. 
         [0082]      FIG. 13  shows an exemplary TDE  1360 , which includes a trip dynamics logger  1361 ; a trip dynamics kernel  1362 ; and a trip dynamics display  1100 . The trip dynamics logger  1361  collects, and stores in tangible storage, data accessed from the CAN  1380 . This data may pertain to any of the components of the vehicle  1300 , 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 logger  1361  from other sources (not shown), such as weather stations and Internet websites, research facilities, or company or government databases. 
         [0083]    The trip dynamics kernel  1362  may analyze data, communicate information, and cause actions to be taken. The trip dynamics kernel  1362  may compute the variables such as those in the tables of  FIG. 1-10 , possibly using a vehicle  1300  model such as that of  FIG. 13 , combined with a physical dynamics model such as that illustrated by the equation tables of  FIG. 19-28 . The kernel  1362  may produce and manage a trip dynamics display  1100  as exemplified by  FIG. 11 . 
         [0084]    Note in  FIG. 13  that arrow  1381  is double headed. In some embodiments of the invention, the kernel  1362  may determine that the vehicle  1300  itself is operating suboptimally, and send a command to the ECU  1321  or other component or system, causing the vehicle  1300  to change its behavior. 
         [0085]    Hardware components of a TDE  1360  may be located in the vehicle  1300 , or they may be remote from the vehicle  1300 . 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. 
         [0086]      FIG. 14  is an exemplary TDE  1360  showing more detail, particularly of an exemplary trip dynamics logger  1361 . This trip dynamics logger  1361  can be inserted into a connector in the vehicle  1300 . Such a connector, such as a J1939 connector  1406  is fairly standard in modern heavy-duty vehicles  1300 . The connector  1406  puts trip dynamics logger  1361  into communication with the CAN  1380 . The trip dynamics logger  1361  includes a microprocessor  1400  to execute logic and access data; firmware  1401  to store instructions and data; a GPS  1402  device to locate the vehicle  1300  in three-space—note that another trip dynamics logger  1361  might include other environmental sensors; tangible storage (removable storage  1407  in 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 kernel  1362 , the display  1100  or with external resources  1409 —in this example, namely BLUETOOTH  1403 , Global System for Mobile Communications (GSM)  1404 , and Wi-Fi  1405 . The trip dynamics kernel  1362  and/or logic for the display  1100  may be running in the microprocessor  1400  of the trip dynamics logger  1361  or in some other microprocessor. 
         [0087]      FIG. 15  illustrates a tree of parameters that may be used to create a chart  1101  and performance statistics  1120  like  FIG. 11 . Many of these parameters are in the variables tables, or were described in connection with  FIG. 11  itself. The remaining parameters are user preferences for the chart  1101 . These include the throttle step  1501  (i.e., the separation between tick marks on the throttle axis); the symbol  1510  for the current operation point, as well as its size  1511 , color  1512 , and animation  1513 ; and the symbol  1520  for the best operation point, as well as its size  1521 , color  1522 , and animation  1523  (Color or animation can be used to distinguish certain points on the chart  1101  in lieu of the shading that was used in  FIG. 11 .) 
         [0088]      FIG. 15  illustrates a tree of parameters that may be used to create a chart  1101  and performance statistics  1120  like  FIG. 11  in near-real-time. Most of these parameters were already described either in the variables tables, or in connection with  FIG. 11  itself. The remaining parameters are user preferences for the chart  1101 . These include the throttle step  1501  (i.e., the separation between tick marks on the throttle axis); the symbol  1510  for the current operation point, as well as its size  1511 , color  1512 , and animation  1513 ; and the symbol  1520  for the best operation point, as well as its size  1521 , color  1522 , and animation  1523 . (Color or animation can be used to distinguish certain points on the chart  1101  in lieu of the shading that was used in  FIG. 11 .) 
         [0089]    The trip dynamics kernel  1362  uses a model of the vehicle  1300 , such as shown in  FIG. 13 , to calculate as necessary any of the display components, possibly using data saved by the trip dynamics logger  1361 .  FIG. 16  illustrates some of the kinds of processes that may be executed by a trip dynamics kernel  1362 . The trip dynamics kernel  1362  may compute engine brake torque  1601 ; compute torque converter output torque  1602 ; compute clutch output torque  1603 ; compute rear axle output speed  1604 ; compute rear axle output torque  1605 ; compute maximum fuel economy  1606 ; compute maximum drivability  1607 ; compute fuel economy  1608 ; compute drivability  1609 ; compute score  1610 ; compute acceleration  1611 ; compute fuel economy score  1612 ; and compute drivability score  1613 . These processes can be used to populate the UI  1130  of the trip dynamics display  1100  and for many other purposes. 
         [0090]    A trip dynamics kernel  1362  that has available a physical dynamics model as illustrated by  FIG. 19-28  can implement logic to compute a set of variables, such as illustrated by  FIG. 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 kernel  1362  from 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”. 
         [0091]    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 in  FIG. 16 , as well as many more not explicitly listed there, are fully supported in this Description and the drawings. 
         [0092]    The above method for obtaining a process whereby any target variable in the variables tables can be sourced or calculated is summarized by  FIG. 17 . After the start  1700 , traverse  1710  backward through the tree of source equations to find the base source variables on which the target variable depends. Obtain  1720  the values of those base source variables. Apply  1730  the source equations already found to calculate the target variable from the values of the base source variables. The method ends  1730 . 
         [0093]    The method of  FIG. 17  can be used to specify a process to find any variable from  FIG. 15  that is included in the variables tables. In effect,  FIG. 17  is a metaprocess that teaches processes for computing every variable in an embodiment of the dynamics model. 
         [0094]      FIG. 18  illustrates the method of  FIG. 17  for the overall goodness score  113  variable used in the chart  1101  of  FIG. 11 .  FIG. 18  illustrates relationships among the variables of  FIG. 1-FIG .  10 , the equations of  FIG. 19-28 , and the processes of the trip dynamics kernel  1362  shown in  FIG. 16 . (Note, however, that embodiments may differ with respect to equations, variables, and sources of particular variables.) 
         [0095]    In  FIG. 18 , variables in the vehicle dynamics model, such as score  113  and average fuel economy  304  are 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 factor  106 , maximum drivability  109 , and maximum fuel economy  305  are base source variables, derived from the respective sources user preference  1801 , historical statistics  1802 , and manufacturer specification  1803 , which are shown in rounded rectangles. The users, or stakeholders, that might specify or influence user preferences  1801  include, for example, the driver  1350 , a fleet owner/operator, a manufacturer, a supplier, a vehicle designer, a governmental entity, and an organization (e.g., environmental, energy, political). 
         [0096]    If a variable is not a base source variable, it may be computed from an equation. Equation numbers that correspond to  FIG. 19-28  are shown parenthetically in  FIG. 18 . For example, score  113  is computed from equation (5). As shown in  FIG. 16 , the trip dynamics kernel  1362  may compute score  1610  as one of its functions, and  FIG. 18  shows that equation (5) indicates a process for doing so. 
         [0097]    Accordingly, score  113  (in this particular embodiment) is found in equation (5) to depend directly on four variables, namely, fuel economy score  104 , drivability score  110 , fuel economy weight factor  106 , and drivability weight factor  112 . As taught by  FIG. 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 score  113 . 
         [0098]    In fact, recursion through this particular tree may involve nearly all variables and equations in the model. Triangle  1810  indicates that the process compute fuel economy  1608  to compute instantaneous fuel economy at steady state  303  uses equation (23), the tree expansion of which is omitted from  FIG. 18 . Similarly, triangle  1811  indicates that the process compute drivability  1609  to compute instantaneous drivability  107  uses equation (2), the tree expansion of which is also omitted. Note, however, that  FIG. 18  merely presents in an alternative form relationships that are already defined, comprehensively for this embodiment, by  FIGS. 1-10  and  19 - 28 . 
         [0099]    A few closing remarks about  FIG. 18  are in order. As the figure illustrates, the model configuration allows the trip dynamics kernel  1362  to calculate any of the variables in the tables. We conclude that  FIG. 16  lists only a few of the processes that are taught by this Specification for certain embodiments of the invention. Also,  FIG. 16  includes the process—compute maximum fuel economy  1606 —while in  FIG. 18 , maximum fuel economy  305  is a base source variable obtained from manufacturer specification  1803 . This illustrates that there may be more than way to obtain some of these variables. Similarly,  FIG. 16  includes the process—compute maximum drivability  1607 —while in  FIG. 18 , maximum drivability  109  is a base source variable obtained from historical statistics  1802 , possibly obtained by the trip dynamics logger  1361  from observation of this or similar vehicles  1300 . 
         [0100]    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.