CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims the benefit of U.S. Provisional Ser. No. 61/619,968, filed Apr. 4, 2012. 
    
    
     GOVERNMENT INTEREST 
     This invention was made, at least in part, under U.S. Government, Department of Energy, Contract No. DE-EE0002720. The Government may have rights in this invention. 
    
    
     FIELD 
     The present disclosure relates to vehicle powertrain control and, more specifically, to predictive vehicle powertrain control based on powertrain history and GPS data. 
     BACKGROUND 
     Motorized vehicles include a powertrain operable to propel the vehicle and power the onboard vehicle electronics. The powertrain typically includes an engine that powers the final drive system through a multi-speed transmission. Many of today&#39;s conventional, gas-powered vehicles are powered by an internal combustion (IC) engine. 
     Hybrid vehicles have been developed and continue to be developed. Conventional hybrid electric vehicles (HEVs) combine internal combustion engines with electric propulsion systems to achieve better fuel economy than non-hybrid vehicles. Plugin hybrid electric vehicles (PHEVs) share the characteristics of both conventional hybrid electric vehicles and all-electric vehicles by using rechargeable batteries that can be restored to full charge by connecting (e.g. via a plug) to an external electric power source. 
     Despite the introduction of hybrid vehicles and improved conventional gas powered vehicles, the automotive industry is continually faced with the challenge of improving fuel economy and reducing emissions without sacrificing vehicle performance. As mentioned above, there are many different types of vehicles in existence today with numerous others being developed for the future. Accordingly, there is a need and desire for a technique for improving fuel economy and reducing emissions without sacrificing vehicle performance that will work with many different types of vehicles. 
     SUMMARY 
     In one form, the present disclosure provides a method of controlling a vehicle powertrain. The method comprises determining a present location of the vehicle; predicting a route of travel for the vehicle from the present location based on the current day and time; retrieving historical powertrain loads and speeds for the predicted route of travel; and optimizing a powertrain operation based on the retrieved historical powertrain loads and speeds. 
     The present disclosure also provides a powertrain apparatus for a vehicle. The apparatus comprises a controller adapted to determine a present location of the vehicle; predict a route of travel for the vehicle from the present location based on the current day and time; retrieve historical powertrain loads and speeds for the predicted route of travel; and optimize a powertrain operation based on the retrieved historical powertrain loads and speeds. 
     In one embodiment, wherein the optimized powertrain operation comprises one of shift scheduling and battery control. 
     In another embodiment, predicting the route of travel comprises searching recorded history to determine if the present location is associated with another historical location that is typically traveled after the present location for a matching day and time; and adding the another historical location to the predicted route of travel if it is determined that the present location is associated with another historical location that is typically traveled after the present location for a matching day and time. In another embodiment, the other historical location is not added if additional information indicates that a different location should be added to the predicted route. The additional information may be input from a vehicle to vehicle data source or a vehicle to infrastructure data source. 
     Further areas of applicability of the present disclosure will become apparent from the detailed description provided hereinafter. It should be understood that the detailed description, including disclosed embodiments and drawings, are merely exemplary in nature intended for purposes of illustration only and are not intended to limit the scope of the invention, its application or use. Thus, variations that do not depart from the gist of the invention are intended to be within the scope of the invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates a predictive powertrain control system constructed in accordance with an embodiment disclosed herein; and 
         FIG. 2  illustrates in flowchart form a predictive powertrain control method operating in accordance with an embodiment disclosed herein. 
     
    
    
     DETAILED DESCRIPTION 
     According to the principles disclosed herein, and as discussed below, predictive control of the powertrain of various conventional and hybrid vehicles can be performed to improve fuel economy and emissions using predicted vehicle usage based on the powertrain&#39;s operational history and GPS information. According to the principles disclosed herein, powertrain history including speeds and loads, along with present and past GPS location are used to predict future loads and speeds for the current trip. In addition, a navigational system, map database and driver inputs will not be required for the method and system disclosed herein to make a prediction of the driver&#39;s route and intended destination. 
     Predicting the loads and speeds of the vehicle for the duration of a trip allows shift scheduling to be performed on conventional vehicles and allows transmission and battery control to be performed on HEVs and PHEVs. For example, predicted trip load and grades can be used to optimize battery charging and discharging locations along the trip. Moreover, the modes of transmission operation (e.g., in electrically variable transmissions) or gear ratio selection on conventional and other HEVs can be optimized. For PHEVs, which are generally designed to operate in two modes (a charge depleting mode or a charge sustaining mode), prediction of battery charging locations can be used to change battery discharging strategy (in the charge depleting mode) to be more or less aggressive. 
     As will be shown below, GPS and powertrain speeds and loads along the route traveled are stored with a time stamp in a non-volatile memory. When a driver travels on the path again, and a similar day and time match is found in the stored history information, the corresponding data is retrieved. Thus, if a good match in the past history is found based on the first few maneuvers of the driver along the route, then the driver is determined to be traveling to that destination. Past days and times are compared for this purpose and similar days and times are weighted higher to determine the match. If the vehicle deviates from the best match for the route, alternatives are searched. If no past routes are matched with the present travel, the powertrain controls will default to non-predictive operation. 
     As will be discussed below with reference to  FIG. 2 , the past speeds and loads for the identified route are adjusted for present conditions (e.g., weather). The predicted speeds and loads will be used to control and optimize the powertrain to improve the fuel economy and emissions in a manner that will not impact the vehicle&#39;s driving performance. That is, the predicted speeds and loads on the powertrain can be used to optimize the shift scheduling of conventional vehicles and the transmission and battery control for HEVs and PHEVs. 
       FIG. 1  illustrates a predictive powertrain control system  10  constructed in accordance with an embodiment disclosed herein. The system  10  has a predictive powertrain controller  40 , which may be a programmed processor or other programmable controller suitable for performing the method  100  illustrated in  FIG. 2  and discussed below in more detail. Associated with the controller  40  is a non-volatile memory  42 , which may be part of the controller  40  or a separate component. It should be appreciated that any form of non-volatile memory may be used for memory  42 . In addition, the predictive powertrain control programming is stored in the memory  42 . It should be appreciated that the functions performed by the controller  40  can also be integrated into the vehicle&#39;s powertrain control software, if desired. 
     As can be seen in  FIG. 1 , the predictive powertrain controller  40  receives data and signals from various sources within the vehicle and external to the vehicle. Specifically, the controller inputs data from one or more internal data sources  18  (e.g., speedometer, accelerometer) and driver input information from e.g., the steering column  12 , accelerator pedal sensor  14  and brake pedal sensor  16 . It is desirable for the controller  40  to be connected to one or more navigation data sources  20  (e.g., compass or GPS receiver) and one or more external data sources such as a vehicle to vehicle data source  32  and a vehicle to infrastructure data source  34 . The input information/data can include e.g., expected speeds and speed limits (e.g., from vehicle to infrastructure data sources such as smart traffic lights, highway information systems, etc.), weather conditions (e.g., wet, dry, icy, windy, etc. from weather service information input e.g., from GPS, vehicle to vehicle or vehicle to infrastructure data sources) or any other information provided by or transmitted by the various illustrated data sources. 
       FIG. 2  illustrates an example predictive powertrain control method  100  according to the principles discussed herein. The method  100 , at step  102 , records the present GPS location with a time stamp including the day of week as well as the time of day. At step  104 , the present speed and load on the powertrain are recorded with the same time stamp from step  102  (i.e., a time stamp including the day of week as well as the time of day). These steps are periodically performed throughout the trip and are used in the predictive portion of the method  100  as described below in more detail. 
     The predictive portion of the method  100  begins at step  106  where the present GPS location of the vehicle is determined. It should be appreciated that the location recorded in step  102  may be used and that step  106  may be skipped, if desired. At step  108  a search of the recorded history is made to determine if the current GPS location is associated with another historical GPS point that is typically traveled immediately after the current GPS location. The search looks for a best match based on a matching time of day and day of week. If it is determined that there is another (or more) historical GPS point traveled after the current GPS point, the method  100  includes the next historical GPS point in the current predicted route (step  110 ) and continues at step  108  to check for another “next” point on the predicted route. Thus, steps  108  and  110  add GPS points to the predicted route based on route information previously stored in the map database that matches the current day and time of day. The route information preferably includes historical battery charging locations, which are also factors for optimizing the powertrain of PHEVs and similar vehicles. As mentioned above, the prediction of battery charging locations can be used to change battery discharging strategy to be more or less aggressive. 
     If at step  108  it was determined that there is no next GPS point in the stored history that matches the time of day and day of week, the method continues at step  112  where stored powertrain loads and speeds corresponding to the predicted route are retrieved from the stored history. Using the retrieved powertrain loads and speeds, at step  114 , an optimized powertrain control strategy is then developed for the type of vehicle. For conventional vehicles, this means that e.g., shift maps for a shifting schedule can be modified. For HEVs and PHEVs, battery charging and discharging scheduling can be modified based on the desired aggressiveness of the schedule. Relevant engine commands needed to implement the new strategy are also developed. The predictive powertrain control strategy is then executed at step  116 . 
     It should be appreciated that the disclosed system  10  and method  100  enhance the real world fuel economy of the vehicle, allowing the vehicle&#39;s owner to save money on fuel. Better fuel economy is also beneficial to the environment because less fuel is being consumed and less emissions are entering the atmosphere. The disclosed system  10  and method  100  capitalize on information that is readily available from onboard components and systems already present within the vehicle. Moreover, the system  10  and method  100  do not need a navigation system to operate successfully. As such, the system  10  and method  100  are easily and inexpensively implemented into the vehicle. Moreover, the system  10  and method  100  disclosed herein do not require the driver to enter a route or other information to successfully operate and improve the vehicle&#39;s fuel economy.
A method and powertrain apparatus that predicts a route of travel for a vehicle and uses historical powertrain loads and speeds for the predicted route of travel to optimize at least one powertrain operation for the vehicle.