Patent Publication Number: US-2015073933-A1

Title: Vehicle powertrain selector

Description:
BACKGROUND 
     A given model of a vehicle such as an automobile is generally offered with multiple powertrain options. Different powertrains may require different fuels, offer different fuel efficiencies, perform differently in different environments and/or in response to different driving styles, etc. Further, different consumers may drive on different roads, may drive in different geographic areas with different environmental conditions, and have different driving styles (e.g., drive faster, accelerate more quickly than average), etc. Different consumers may experience different fuel economies with respect to a particular vehicle powertrain, even under similar driving conditions. Unfortunately, mechanisms are lacking for determining what powertrain configuration best suits a particular consumer&#39;s driving needs. 
    
    
     
       DRAWINGS 
         FIG. 1  is a block diagram of an exemplary system for generating powertrain recommendations. 
         FIG. 2  is a diagram of an exemplary process for generating a powertrain recommendation for a particular vehicle operator for a particular type of vehicle. 
         FIG. 3  is a diagram of an exemplary process including details of generating recommendations for vehicle-powertrain pairs and/or powertrains in a specific make and model of a vehicle using machine learning techniques. 
         FIG. 4  illustrates an exemplary process including details of generating recommendations for vehicle-powertrain pairs and/or powertrains in a specific make and model of a vehicle using computer simulation techniques. 
     
    
    
     DETAILED DESCRIPTION 
     Exemplary System Overview 
       FIG. 1  is a block diagram of an exemplary system  100  for generating powertrain recommendations  140 . The system  100  may include one or more vehicles  101 , each vehicle  101  including a vehicle computer  105 . One or more data collectors  110  in each vehicle  101  provide information to the vehicle computer  105  concerning the various metrics related to operation of the vehicle  101 , such information being stored and/or transmitted via a network  120  as usage data  115 . In general, the usage data  115  includes information relating to a driver&#39;s driving habits that may be relevant to formulating a powertrain recommendation  140 . A server  125  receives the usage data  115 , generally via the network  120 . A determination module  130  included in the server  125  uses the usage data  115  and base data  135  to generate a powertrain recommendation  130 . A user device  150  may be used for various purposes, including accessing a powertrain recommendation  140  from the server  125  via the network  120 . 
     Exemplary System Elements 
     A vehicle  101  includes a vehicle computer  105  that generally includes a processor and a memory, the memory including one or more forms of computer-readable media, and storing instructions executable by the processor for performing various operations, including as disclosed herein. The memory of the computer  105  further generally stores usage data  115 . The computer  105  is generally configured for communications on a controller area network (CAN) bus or the like. The computer  105  may also have a connection to an onboard diagnostics connector (OBD-II). Via the CAN bus, OBD-II, and/or other wired or wireless mechanisms, the computer  105  may transmit messages to various devices in a vehicle and/or receive messages from the various devices, e.g., controllers, actuators, sensors, etc., including data collectors  110 . Note the computer  105  could include one or more various devices, e.g., an in-vehicle computer, a mobile computer such as a smartphone, etc. 
     Data collectors  110  may include a variety of devices. For example, various controllers in a vehicle may operate as data collectors  110  to provide data  115  via the CAN bus, e.g., data  115  relating to vehicle speed, acceleration, etc. Further, sensors or the like, global positioning system (GPS) equipment, etc., could be included in a vehicle and configured as data collectors  110  to provide data directly to the computer  105 , e.g., via a wired or wireless connection. 
     Usage data  115  may include a variety of data collected in one or more vehicles based on operations by a particular consumer, i.e., vehicle user. Data  115  is generally collected using one or more data collectors  110 , and may additionally include data calculated therefrom in the computer  105 , and/or at the server  125 . Further, usage data  115  could include data gathered from user input concerning driving habits, e.g., a survey could be presented via an interface of the computer  105  or via some other mechanism (e.g., a website), requesting that a user provide items of data  115 . For example, a user could indicate whether and/or how often the user tows trailers (and a typical trailer weight), carries items on roof racks (and what types of items), drives off-road, uses the vehicle for racing, takes long trips, and/or other types of vehicle  101  usage that might not be captured during a limited time of data acquisition. Likewise, usage data  115  concerning a user or group of users could be provided via other sources, e.g., data from a customer relationship management (CRM) database, data concerning fleet operations, etc. 
     In general, usage data  115  may include any data that may be gathered and/or computed, and that may be relevant to vehicle powertrain usage. For example, usage data  115  may include vehicle speed, vehicle  101  acceleration, percent of time at idle, average trip duration, average distance driven per trip, ambient outside temperature, geographic locations and time spent thereat, fuel consumption data, etc. 
     As seen in  FIG. 1 , system  100  may include a plurality of vehicles  101 , although it should be understood that the systems and methods disclosed herein regarding generating predictions with respect to vehicle powertrains may operate for one vehicle  101  or a fleet of vehicles  101 . 
     The network  120  represents one or more mechanisms by which a vehicle computer  105  may communicate with a remote server  125 . Accordingly, the network  120  may be one or more of various wired or wireless communication mechanisms, including any desired combination of wired (e.g., cable and fiber) and/or wireless (e.g., cellular, wireless, satellite, microwave, and radio frequency) communication mechanisms and any desired network topology (or topologies when multiple communication mechanisms are utilized). Exemplary communication networks include wireless communication networks, local area networks (LAN) and/or wide area networks (WAN), including the Internet, providing data communication services. 
     The server  125  may be one or more computer servers, each generally including at least one processor and at least one memory, the memory storing instructions executable by the processor, including instructions for carrying out various of the steps and processes described herein. Such instructions include instructions in various modules such as a determination module  130  that includes simulators, and/or machine learning techniques, e.g., neural network classifiers, to generate powertrain recommendations  140 . 
     Base data  135  includes various sets of data, each set of base data  135  including operating parameters and/or default performance characteristics of a particular powertrain configuration of a particular model of vehicle  101 . For example, base data  135  may include default performance characteristics such as fuel economy estimates for a particular powertrain configuration calculated according to one or more predefined metrics, e.g., fuel economy estimates calculated according to rules promulgated by the United States Environmental Protection Agency at 40 C.F.R. §600.114-08. Further, base data  135  may include operating parameters of a powertrain, such as acceleration curve showing how quickly a vehicle  101  with the powertrain accelerates from zero to various speeds over time, engine size, transmission configuration (number of speeds, automatic/manual), etc. Further, base data  135  may include parameters associated with the particular model of vehicle  101 , such as curb weight, aerodynamic drag coefficient, tire rolling resistance, etc., as well as effects on these parameters resulting from added accessories (trailer, roof rack, etc.) or loads (cargo weight, etc.). 
     In general, a powertrain recommendation  140  may be generated according to a deterministic simulation and/or a statistical prediction of likely powertrain usage based on usage data  115  and base data  135 . The determination module  130  may operate by employing predictive modeling techniques, e.g., training one or more neural networks and/or other machine learning techniques that accept collected data  115  as input, and provide as output weighting factors that may be applied to base data  135  for various powertrains in generating recommendations  140 . Alternatively or additionally, a simulator included in the determination module  130  may use selected collected data  115  and/or selected base data  135  as input, e.g., data specifying factors such as those listed in more detail below, but possibly including an average vehicle speed, distance driven, commuting time, annualized vehicle usage, environmental conditions such as ambient temperature, usage of a climate control system, weather conditions, etc., to run a simulation generating predictions of a powertrain performance with respect to various characteristics, e.g., fuel economy. 
     In general, a recommendation  140  may include a recommendation for a powertrain configuration of a particular make or model of a vehicle, and/or what may be referred to as a vehicle-powertrain pair, i.e., a recommendation  140  for a particular make and model of a vehicle employing a specific powertrain configuration available for the particular vehicle make and model. A powertrain recommended for a vehicle may include virtually any available powertrain, e.g., a powertrain could include an internal combustion (IC) engine, a manual transmission, an automatic transmission, a hybrid electric powertrain, a plug-in hybrid electric powertrain, a pure electric powertrain, engines using different fuels (gasoline, ethanol blends, diesel, CNG, LPG, etc.), engines using “start-stop” technology, etc. The general objective of a recommendation  140  is to provide a driver (or group of drivers for a vehicle  101  fleet) with advice concerning a powertrain or vehicle-powertrain pair that will provide a likely suitable driving experience (e.g., desired fuel economy, desired acceleration characteristics, desired towing or off-road capability, etc.). 
     A user device  150  may be any one of a variety of computing devices including a processor and a memory, as well as communication capabilities. For example, the user device  155  may be a portable computer, tablet computer, a smart phone, etc. that includes capabilities for wireless communications using IEEE 802.11, Bluetooth, and/or cellular or other communications protocols. Further, the user device  155  may use such communications capabilities to communicate via the network  120  and also directly with a vehicle computer  105  and/or data collectors  110 , e.g., using a CAN bus, OBD-II, Bluetooth, and/or other wired or wireless mechanisms. 
     Exemplary Process Flows 
       FIG. 2  is a diagram of an exemplary process  200  for generating a powertrain recommendation  140  for a particular vehicle operator for a particular type of vehicle  101 , or for a group of operators using a fleet of vehicles  101 . For example, the process  200  may be carried out by the server  125  according to instructions included in the determination module  130 . 
     The process  200  may begin in a block  205 , in which the server  125  obtains usage data  115  related to a particular vehicle user, or, as in the case where a recommendation  140  is being obtained for a fleet of vehicles  101 , the data  115  may be related to a plurality of vehicle users; in any event, the data  115  may be obtained from one or more vehicle computers  105  and/or user devices  150 . Usage data  115  is generally provided in conjunction with an identifier for a vehicle  101  from which the usage data  115  was provided, and also generally with an identifier for an operator of the vehicle  101 . Base data  135  is also generally associated with an identifier for a vehicle  101 . Note that identifying a vehicle  101  generally includes, in addition to identifying a make and model of the vehicle  101 , further identifying a specific type of vehicle  101 , i.e., a trim level including a specific powertrain configuration of the vehicle  101 . Further, in some implementations, a user may specify particular vehicles  101  and/or vehicle-powertrain pairs (explained further below) for which the user would like recommendations  140 , e.g., “show me which powertrain among Fusions is best,” meaning that only base data  135  relating to Ford Fusions is used, or “compare gasoline Fusion to a gasoline Focus,” meaning that only base data  135  for those two vehicle-powertrain pairs is considered. 
     Next, in a block  210 , the server  125  inputs the usage data  115  and the base data  135  to determination module  130 , e.g., to a simulation, a machine classifier such as a neural network, etc. Exemplary operations of the determination module  130  are discussed in further detail below with respect to  FIGS. 3 and 4 . 
     Next, in a block  215 , the determination module  130  outputs a powertrain recommendation  140 . The server  125  may provide the recommendation  140  to a user via a variety of mechanisms, e.g., via a printed report, webpage, email, short message service (SMS) text message, etc. In general, the recommendation  140  may identify a specific powertrain of a vehicle  101  recommended for a user, i.e., a consumer whose usage data  115  has been input to the module  130 , and/or can include a predicted fuel economy for the user for one or more powertrains. Further, as mentioned above, a recommendation  140  may include a recommendation for what may be referred to as a vehicle-powertrain pair, i.e., a recommendation  140  for a particular make and model of a vehicle employing a specific powertrain configuration available for the particular vehicle make and model. 
     Following the block  215 , the process  200  ends. 
       FIG. 3  illustrates an exemplary process flow  300  including additional details of operations of the determination module  130  for generating recommendations  140  for vehicle-powertrain pairs and/or powertrains in a specific make and model of a vehicle  101  using machine learning techniques. 
     The process  300  begins in a block  305 , in which the server  125  obtains base data  135  for the vehicle  101  type or types being considered. For example, fuel economy information included in base data  135  for a vehicle  101  type is generally publicly available and/or maintained by a vehicle  101  manufacturer. Publicly available fuel economy data may include regulatory sources such as EPA data, and consumer sources such as Consumer Reports, automotive review magazines, etc. Further, a vehicle  101  manufacturer or publicly available source may maintain base data  135  relating to vehicle  101  acceleration characteristics, engine size, transmission configuration (number of speeds, automatic/manual), fuel type, degree of hybridization, etc. 
     Next, in a block  310 , the server  125  obtains training data related to operations of the vehicle  101 . For example, a vehicle  101  manufacturer or other party may operate the vehicle  101  in a test environment, in a road test, etc., to obtain initial usage data  115  for use as the initial set of training data. Further, in subsequent iterations of the process  300 , usage data  115  may be provided as training data in the block  310 . 
     Next, in a block  315 , the server  125  creates an initial model, e.g., using a set of neural networks, for generating recommendations  140  using the training data obtained in the block  310 . (It should be understood that, for purposes of the process  300 , “creating” a model could refer to training and modifying an existing model, and does not necessarily refer to creating a totally new model.) Recommendations  140  are generally based on estimates of one or more operating characteristics of a vehicle  101 . An example of an operating characteristic of a vehicle  101  is fuel economy, although other examples are possible, such as factors affecting vehicle acceleration such as engine size, vehicle weight, etc. Further, fuel economies may be calculated or obtained in a variety of ways. Further, multiple models may be created in the block  315 , e.g., one for each vehicle-powertrain configuration that may be considered in the process  300 . 
     For example, a model could include a set of neural networks configured to generate a probability that operation of the vehicle  101  would approximate known fuel economy standards, e.g., the so-called “five-cycle” fuel economy labels promulgated by the United States Environmental Protection Agency. These estimates of fuel economy include the “city” driving estimated by federal test procedure (FTP) FTP-75 and the “highway” driving estimated by the Highway Fuel Economy Test (HWFET). In addition, the US EPA estimates generally include the so-called Supplemental Federal Test Procedure (SFTP) tests SFTP US06 (high-speed, moderate ambient temperature, no air-conditioning), SFTP SC03 (air-conditioning test at 95° Fahrenheit), and a cold FTP test that is generally the same as the city cycle, except performed at an ambient temperature of 20° Fahrenheit. 
     As mentioned above, 40 C.F.R. §600.113-08 provides “Fuel economy calculations for FTP, HWFET, US06, SC03 and cold temperature FTP tests.” Accordingly, each of the five EPA estimates may be represented by a respective set of one or more equations, as is known. Creating a model in the block  315  may include training a neural network for each of the five estimates for a type of vehicle  101  to provide a probability or weighting factor for each of the respective fuel economy estimates. Possible weighting factors are discussed below with respect to the block  325 . 
     Next, in a block  320 , the server  125  obtains usage data  115  from one or more users&#39; operation of one or more vehicles  101 , e.g., transmitted from a computer  105  and/or user device  150  via the network  120 . Mechanisms by which the computer  105  and/or user device  150  may gather usage data  115  are discussed above. 
     Next, in a block  325 , the server  125  inputs the usage data  115  into the model(s) created as described with respect to the block  315 , and generates one or more predicted operating characteristics for operation of respective vehicle-powertrain pair or a powertrain. For example, the predicted operating characteristics could include re-calculating weighting factors applied to each of five fuel economy test cycles for the vehicle  101 , thereby predicting an overall fuel economy for a specific user of a type of vehicle  101 , e.g., for a specific powertrain. For example, weighting factors could be calculated based on:
         calculation of average trip lengths in usage data  115 , followed by a calculation of “start” penalties, i.e., taking into account that shorter trips have poorer fuel economy than longer trips due to the poorer efficiency of “cold” powertrains;   calculation of maximum or near-maximum trip lengths in usage data  115 , e.g. 95th percentile or 99th percentile trip lengths (this factor is particularly important in evaluating the feasibility of powertrains and/or vehicles that use alternate fuels, based on fuel availability in the customer&#39;s area, and/or, in the case of electric vehicles, battery size);   calculation of typical time spent at idle in usage data  115 ;   trip frequency in usage data  115 , e.g., time between trips;   proximity to refueling/recharging stations during trips in usage data  115 ;   calculation of “running” fuel economy at 75° Fahrenheit based on specific power for a vehicle  101  type (e.g., based on velocity, acceleration, road loads, and mass);   Adjustment for ambient temperatures in usage data  115 , e.g., where ambient temperatures are cold, i.e., below a certain threshold; note that ambient climate data may be inferred based on a geographic location;   Adjustment for HVAC (heating, ventilation, and air-conditioning) usage, especially air-conditioning; note that annual-average HVAC usage can be inferred based on climate data in a geographic location, optionally modified with limited observation of the customer&#39;s HVAC usage; further, note that HVAC usage has a strong effect on fuel economy for hybrid-electric (HEV) and stop-start vehicles, and on driving range for electric vehicles (EVs).;   adjustment for non-dyno effects, e.g., plus or minus 10% for factors such as hills, wind, precipitation, rough roads, etc.;   typical frequency and duration of various vehicle speeds in usage data  115 ;   typical frequency, duration, and rates of acceleration in usage data  115 ;   typical on-vehicle passenger and cargo weight;   whether towing a trailer (and, if so, weight and road load of trailer);   whether carrying items on the roof (and, if so, added road load due to aerodynamic drag);   snow plow usage;   altitude;   terrain (amount of hill climbing);   off-road usage;   whether a user&#39;s typical driving area is one where alternate fuel (e.g., ethanol, diesel, CNG, LPG, electric vehicle charging station) is readily available.       

     Next, in a block  330 , the server  125  may compare powertrain parameters for the vehicle  101  included in base data  135  with parameters provided from usage data  115  for the type of vehicle  101 , or some other type of vehicle  101  that is similar. Further, the server  125  may evaluate vehicle-powertrain pairs to be included in a recommendation  140 , in which case base data  135  for more than one vehicle  101  may be considered. In any event, a driver may have certain driving habits, e.g., a “lead foot” or the like such that the driver regularly accelerates to a high-speed in a small amount of time. Base data  135  may indicate that the particular vehicle  101  powertrain will not support such acceleration habits. Similarly, usage data  115  may indicate that the driver frequently tows heavy trailers, and again, base data  135  may indicate that the particular vehicle  101  powertrain will not support towing heavy loads. Based on the block  330 , vehicles  101  having certain powertrain configurations may be excluded from possible inclusion in a recommendation  140 . 
     Next, in a block  335 , the server  125 , e.g., the determination module  130 , uses the predicted operating characteristic(s) determined as described above, and generally also the comparison of the block  330 , to generate one or more powertrain, or vehicle-powertrain, recommendations  140 . For example, the determination module  130  may be configured to generate a recommendation  140  for a vehicle  101  having a powertrain that will provide a driver with the best possible fuel economy. However, other considerations may be taken into account. For example, as mentioned above, vehicles  101  having powertrains that are physically incompatible with a driver&#39;s usage data  115  may be excluded. Similarly, driving habits may be taken into account; for example, a user with a penchant for rapid acceleration might receive a recommendation  140  for a powertrain including a largest available engine. Further for example, based on driving habits, fuel economy predictions, and/or powertrain characteristics, a total cost of vehicle ownership, e.g., on a monthly, annual, etc., basis, could be predicted and provided. 
     Following the block  335 , the process  300  ends. 
       FIG. 4  illustrates an exemplary process flow  400  including additional details of operations of the determination module  130  for generating recommendations for vehicle-powertrain pairs and/or powertrains in a specific make and model of a vehicle  101  using computer simulation techniques. 
     The process  400  begins in a block  405 , in which, similar to the block  305  discussed above, the server  125  obtains base data  135  for the vehicle  101  type or types being considered. 
     Next, in a block  410 , in a manner similar to that discussed above concerning the block  320 , the server  125  obtains usage data  115  from one or more users&#39; operation of one or more vehicles  101 . 
     Next, in a block  415 , the server  125  uses base data  135  and usage data  115  to run one or more computer simulations to determine likely fuel economies, for various vehicle-powertrain pairs and/or powertrain configurations for a specific vehicle. For example, a simulator such as Simulink® from MathWorks of Natick, Mass., U.S.A., may be used. The simulator may be configured to use vehicle  101  properties such as vehicle weight, road load, coastdown coefficients, aerodynamic drag coefficient, frontal area, etc. to calculate power required to drive the vehicle at each time step or data point of usage data  135 . The power calculation may be performed for the complete set of usage data  135 , or for a statistically representative subset of usage data  135 . The simulator may further be configured to calculate fuel consumed as a function of power required and other factors including engine speed, transmission gear and/or torque converter lockup state, hybrid powertrain state, etc. Such fuel consumption calculations may be based on data or models for various powertrains available for vehicle  101 . The simulator may further be configured to sum up or integrate total fuel consumed during usage data  135  (or a subset thereof), including fuel required for idle, cold start or trip length penalties, etc. The simulator may further be configured to calculate an average fuel economy for various powertrains or vehicle-powertrain pairs based on usage data  135 , or to calculate statistical ranges of expected fuel economy, e.g. an average and standard deviation. 
     Next, in a block  420 , in a manner similar to that discussed above concerning the block  330 , the server  125  may compare powertrain parameters for the vehicle  101  included in base data  135  with parameters provided from usage data  115  for the type of vehicle  101 . Further, the server  125  may evaluate vehicle-powertrain pairs to be included in a recommendation  140 , in which case, as mentioned above, base data  135  for more than one vehicle  101  may be considered and/or base data  135  for specific vehicle-powertrain pairs may be considered. 
     Next, in a block  420 , in a manner similar to that discussed above concerning the block  335 , the server  125 , e.g., the determination module  130 , uses the predicted operating characteristic(s) determined as described above, and generally also the comparison of the block  330 , to generate one or more powertrain, or vehicle-powertrain, recommendations  140 . 
     CONCLUSION 
     Computing devices such as those discussed herein generally each include instructions executable by one or more computing devices such as those identified above, and for carrying out blocks or steps of processes described above. For example, process blocks discussed above may be embodied as computer-executable instructions. 
     Computer-executable instructions may be compiled or interpreted from computer programs created using a variety of programming languages and/or technologies, including, without limitation, and either alone or in combination, Java™, C, C++, Visual Basic, Java Script, Perl, HTML, etc. In general, a processor (e.g., a microprocessor) receives instructions, e.g., from a memory, a computer-readable medium, etc., and executes these instructions, thereby performing one or more processes, including one or more of the processes described herein. Such instructions and other data may be stored and transmitted using a variety of computer-readable media. A file in a computing device is generally a collection of data stored on a computer readable medium, such as a storage medium, a random access memory, etc. 
     A computer-readable medium includes any medium that participates in providing data (e.g., instructions), which may be read by a computer. Such a medium may take many forms, including, but not limited to, non-volatile media, volatile media, etc. Non-volatile media include, for example, optical or magnetic disks and other persistent memory. Volatile media include dynamic random access memory (DRAM), which typically constitutes a main memory. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EEPROM, any other memory chip or cartridge, or any other medium from which a computer can read. 
     In the drawings, the same reference numbers indicate the same elements. Further, some or all of these elements could be changed. With regard to the media, processes, systems, methods, etc. described herein, it should be understood that, although the steps of such processes, etc. have been described as occurring according to a certain ordered sequence, such processes could be practiced with the described steps performed in an order other than the order described herein. It further should be understood that certain steps could be performed simultaneously, that other steps could be added, or that certain steps described herein could be omitted. In other words, the descriptions of processes herein are provided for the purpose of illustrating certain embodiments, and should in no way be construed so as to limit the claimed invention. 
     Accordingly, it is to be understood that the above description is intended to be illustrative and not restrictive. Many embodiments and applications other than the examples provided would be apparent to those of skill in the art upon reading the above description. The scope of the invention should be determined, not with reference to the above description, but should instead be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. It is anticipated and intended that future developments will occur in the arts discussed herein, and that the disclosed systems and methods will be incorporated into such future embodiments. In sum, it should be understood that the invention is capable of modification and variation and is limited only by the following claims. 
     All terms used in the claims are intended to be given their broadest reasonable constructions and their ordinary meanings as understood by those skilled in the art unless an explicit indication to the contrary in made herein. In particular, use of the singular articles such as “a,” “the,” “said,” etc. should be read to recite one or more of the indicated elements unless a claim recites an explicit limitation to the contrary.