Abstract:
A method according to an exemplary aspect of the present disclosure includes, among other things, learning an energy consumption efficiency of a vehicle in an energy domain by periodically filtering a ratio of a distance traveled to an energy consumed. The learning step is executed by a control module configured to monitor the energy consumption efficiency.

Description:
CROSS REFERENCE TO RELATED APPLICATION 
       [0001]    This application is a continuation of U.S. patent application Ser. No. 14/282,352, which was filed on May 20, 2014. 
     
    
     TECHNICAL FIELD 
       [0002]    This disclosure relates to a vehicle, and more particularly, but not exclusively, to energy consumption efficiency learning in the energy domain via an energy triggered filter. 
       BACKGROUND 
       [0003]    An energy source in necessary to provide power for propelling a vehicle. For example, petroleum based products, such as gasoline or diesel, are the main energy source of conventional vehicles that include internal combustion engines. On the other hand, electrified vehicles are known that utilize one or more electric machines and in some cases an engine that can be used either individually or in combination to propel the vehicle. A high voltage battery typically acts as one energy source for powering such electric machines. 
         [0004]    It may be beneficial to calculate the energy use of a vehicle in order to display a variety of end use features to the vehicle driver. For example, an energy consumption rate may be monitored and used to predict a distance to empty (DTE) value which can be displayed to the vehicle driver to provide information for trip planning, minimizing driving costs, evaluating vehicle performance, etc. One current method used to monitor energy use of a vehicle is a time domain approach. However, other approaches may provide improvements to existing methods. 
       SUMMARY 
       [0005]    A method according to an exemplary aspect of the present disclosure includes, among other things, learning an energy consumption efficiency of a vehicle in an energy domain by periodically filtering a ratio of a distance traveled to an energy consumed. The learning step is executed by a control module configured to monitor the energy consumption efficiency. 
         [0006]    In a further non-limiting embodiment of the foregoing method, the learning step includes monitoring the energy consumption and periodically adapting an energy consumption efficiency prediction. 
         [0007]    In a further non-limiting embodiment of either of the foregoing methods, the learning step includes integrating a speed of the vehicle to obtain the distance traveled and integrating a power consumption of the vehicle to obtain the energy consumed. 
         [0008]    In a further non-limiting embodiment of any of the foregoing methods, the filtering step is selectively performed after the integrating steps. 
         [0009]    In a further non-limiting embodiment of any of the foregoing methods, the method includes multiplying the ratio by a filter constant. 
         [0010]    In a further non-limiting embodiment of any of the foregoing methods, the method includes updating the energy consumption efficiency if the energy consumed since a prior integrator reset is greater than or equal to an energy consumed threshold. 
         [0011]    In a further non-limiting embodiment of any of the foregoing methods, the method includes using a prior energy consumption efficiency if the energy consumed since the prior integrator reset is not greater than or equal to the energy consumed threshold. 
         [0012]    In a further non-limiting embodiment of any of the foregoing methods, the method includes resetting a vehicle speed integral and a power consumption integral if the energy consumed since a prior integrator reset is greater than or equal to an energy consumed threshold. 
         [0013]    In a further non-limiting embodiment of any of the foregoing methods, the method includes utilizing the energy consumption efficiency to calculate at least one end use feature associated with the vehicle. 
         [0014]    In a further non-limiting embodiment of any of the foregoing methods, the at least one end use feature is a distance to empty estimation. 
         [0015]    In a further non-limiting embodiment of any of the foregoing methods, the at least one end use feature is an instantaneous consumption rate display. 
         [0016]    In a further non-limiting embodiment of any of the foregoing methods, the at least one end use feature is at least one of an average consumption rate/efficiency over a trip odometer, a running average consumption rate/efficiency for a current key cycle and a lifetime running average consumption rate/efficiency. 
         [0017]    In a further non-limiting embodiment of any of the foregoing methods, the at least one end use feature is at least one of a grade estimation and a towing load estimation. 
         [0018]    In a further non-limiting embodiment of any of the foregoing methods, the at least one end use feature is an ECO-routing or an ECO-coaching feature. 
         [0019]    In a further non-limiting embodiment of any of the foregoing methods, the method includes suspending the learning step during steep grade driving conditions. 
         [0020]    A vehicle according to another exemplary aspect of the present disclosure includes, among other things, a transmission, a propulsion device coupled to wheels by the transmission, an energy source configured to power the propulsion device, and a control module in electrical communication with the propulsion device and the energy source. The control module is configured to learn an energy consumption efficiency associated with the vehicle in an energy domain. 
         [0021]    In a further non-limiting embodiment of the foregoing vehicle, the control module is configured to update the energy consumption efficiency each time a predefined amount of energy is consumed by the propulsion device. 
         [0022]    In a further non-limiting embodiment of either of the foregoing vehicles, the control module is configured to suspend learning the energy consumption efficiency of the vehicle during steep grade driving conditions. 
         [0023]    In a further non-limiting embodiment of any of the foregoing vehicles, the control module is configured to integrate a speed of the vehicle to obtain a distance travelled and integrate a power consumption of the vehicle to obtain an energy consumed. 
         [0024]    In a further non-limiting embodiment of any of the foregoing vehicles, the control module is configured to filter a ratio of the distance travelled to the energy consumed. 
         [0025]    The embodiments, examples and alternatives of the preceding paragraphs, the claims, or the following description and drawings, including any of their various aspects or respective individual features, may be taken independently or in any combination. Features described in connection with one embodiment are applicable to all embodiments, unless such features are incompatible. 
         [0026]    The various features and advantages of this disclosure will become apparent to those skilled in the art from the following detailed description. The drawings that accompany the detailed description can be briefly described as follows. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0027]      FIG. 1  schematically illustrates a powertrain of a vehicle. 
           [0028]      FIG. 2  schematically illustrates a control strategy for learning an energy consumption efficiency of a vehicle in an energy domain. 
           [0029]      FIG. 3  illustrates a control strategy according to a second embodiment of this disclosure. 
       
    
    
     DETAILED DESCRIPTION 
       [0030]    This disclosure relates to vehicle energy consumption efficiency learning. The vehicle may be an electrified vehicle, a conventional vehicle, or any other vehicle type. In one embodiment, the energy consumption efficiency learning is performed in an energy domain via an energy triggered filter. The learned energy consumption efficiency may be used to calculate one or more end use features associated with the vehicle. These and other features are discussed in greater detail herein. 
         [0031]      FIG. 1  schematically illustrates a vehicle  10 . This disclosure is applicable to any type of vehicle. For example, the vehicle  10  could be a conventional vehicle that is powered by an internal combustion engine, or could be an electrified vehicle that utilizes one or more electric machines in addition to, or as an alternative to, an engine. 
         [0032]    The exemplary vehicle  10  includes a powertrain  12 . The powertrain  12  may include a propulsion device  14  and a transmission  16  that is selectively driven by the propulsion device  14 . The propulsion device  14  may be employed as an available drive source for the vehicle  10 . For example, the propulsion device  14  could include an engine for a conventional vehicle, or an electric machine (i.e., an electric motor, a generator or a combined motor/generator) for an electrified vehicle. 
         [0033]    The transmission  16  may include a gearbox having multiple gear sets (not shown) that are selectively operated using different gear ratios by selective engagement of friction elements such as clutches and brakes (not shown) to establish the desired multiple discrete or step drive ratios. The friction elements are controllable through a shift schedule that connects and disconnects certain elements of the gear sets to control the ratio between a transmission input shaft  19  and a transmission output shaft  20 . The transmission  16  may alternatively be controlled to achieve an infinite number of ratios. These ratios can be achieved through mechanical reconfiguration as in a continuously variable transmission (CVT) or by electrical coordinate of the speeds of electric machines as in an electrically continuously variable transmission (eCVT). The transmission  16  may be automatically shifted from one ratio to another based on various vehicle and ambient operating conditions by an associated control module  28 . The transmission  16  then provides powertrain output torque to the transmission output shaft  20 . The transmission output shaft  20  may be connected to a differential  22 . The differential  22  drives a pair of wheels  24  via respective axles  26  that are connected to the differential  22  to propel the vehicle  10 . 
         [0034]    An energy source  18  may supply power to the propulsion device  14 . The energy source  18  may be a fuel system if the propulsion device  14  is an engine or a high voltage battery if the propulsion device  14  is an electric machine. For example, an engine is configured to consume fuel (i.e., gasoline, diesel, etc.) to produce a motor output, whereas the high voltage battery is configured to output and receive electrical energy that is consumed by the electric machine to produce a motor output. 
         [0035]    The powertrain  12  of the vehicle  10  may additionally include an associated control module  28 . While schematically illustrated as a single module, the control module  28  may be part of a larger control system and may be controlled by various other controllers throughout the vehicle  10 , such as a vehicle system controller (VSC) that includes a powertrain control unit, a transmission control unit, engine control unit, etc. It should therefore be understood that the control module  28  and one or more other controllers can collectively be referred to as a “control module” that controls, such as through a plurality of integrated algorithms, various actuators in response to signals from various sensors to control functions associated with the vehicle  10 . In one embodiment, the various controllers that make up the VSC may communicate with one another using a common bus protocol (e.g., CAN). 
         [0036]    The control module  28  may include a microprocessor or central processing unit (CPU) in communication with various types of computer readable storage devices or media. Computer readable storage devices or media may include volatile and nonvolatile storage in read-only memory (ROM), random-access memory (RAM), and keep-alive memory (KAM), for example. KAM is a persistent or non-volatile memory that may be used to store various operating variables while the CPU is powered down. Computer-readable storage devices or media may be implemented using any of a number of known memory devices such as PROMs (programmable read-only memory), EPROMs (electrically PROM), EEPROMs (electrically erasable PROM), flash memory, or any other electric, magnetic, optical, or combination memory devices capable of storing data, some of which represent executable instructions, used by the control module  28  to control the vehicle  10 . 
         [0037]    The control module  28  may also communicate with various engine/vehicle sensors and actuators via an input/output (I/O) interface that may be implemented as a single integrated interface that provides various raw data or signal conditioning, processing, and/or conversion, short-circuit protection, and the like. Alternatively, one or more dedicated hardware or firmware chips may be used to condition and process particular signals before being supplied to the CPU. 
         [0038]    As schematically illustrated in  FIG. 1 , the control module  28  may communicate signals to and/or from the propulsion device  14 , the transmission  16 , and the energy source  18 . In other words, these devices are in electrical communication with one another. Although not explicitly illustrated, those of ordinary skill in the art will recognize various functions or components that may be controlled by the control module  28  within each of the subsystems identified above. 
         [0039]    Of course, the control logic stored on the control module  28  may be implemented in software, hardware, or a combination of software and hardware in one or more controllers depending upon the particular application. When implemented in software, the control logic may be provided in one or more computer-readable storage devices or media having stored data representing code or instructions executed by a computer to control the vehicle or its subsystems. The computer-readable storage devices or media may include one or more of a number of known physical devices that utilize electric, magnetic, and/or optical storage to keep executable instructions and associated calibration information, operating variables, and the like. 
         [0040]    In one non-limiting embodiment, the control module  28  is configured to control the vehicle  10  based on a learned energy consumption efficiency. The energy consumption efficiency is “learned” in that the control module  28  continuously monitors the energy consumption efficiency (e.g., miles/gallon, miles/kW-hr, etc.) of the vehicle  10  and selectively adapts the energy consumption efficiency based on a detected change in the energy consumption efficiency. One non-limiting method for learning a vehicle&#39;s energy consumption efficiency is described below with respect to  FIG. 2 . 
         [0041]      FIG. 2 , with continued reference to  FIG. 1 , schematically illustrates a control strategy  100  that may be executed by the control module  28  of the vehicle  10  for learning an energy consumption efficiency of the vehicle  10 . The control module  28  may be programmed to employ one or more algorithms in order to perform the exemplary control strategy  100 . In one non-limiting embodiment, at least equations (1) through (3) (presented below) may be programmed into the control module  28  as part of an algorithm for learning the energy consumption efficiency of the vehicle  10 . 
         [0042]    The control strategy  100  may represent control logic that is implemented by the control module  28  using hardware, software, or a combination of hardware and software. For example, the various functions may be performed using a programmed microprocessor. The control logic may be implemented using any of a number of known programming or processing techniques or strategies and is not limited to the order or sequence illustrated. 
         [0043]    In one non-limiting embodiment, the control strategy  100  learns the energy consumption efficiency of the vehicle  10  in an energy domain. In other words, the energy consumption efficiency is updated at regular energy consumption intervals (i.e., every time a predefined amount of energy is consumed by the vehicle  10 ) as opposed to updating in the time domain at regular time intervals. 
         [0044]    The control strategy  100  may begin by integrating a vehicle speed  101  (such as in kilometers/hour (kph) or miles/hour (mph)) and a power consumption  103  (such as in watts (W) or milligrams of fuel per second (mg/s)) of the vehicle  10  at integrator blocks  102  and  104 , respectively. The vehicle speed  101  and the power consumption  103  are known values. In one embodiment, the vehicle speed  101  and the power consumption  103  are measured, sensed and/or calculated by the control module  28  or by some other component(s) that are in communication with the control module  28  prior to performing the integration at integrator blocks  102 ,  104 . 
         [0045]    The power consumption  103  is not necessarily limited to total power of the vehicle  10 . Power consumption  103  can refer to propulsive power. Other loads like climate loads, accessory loads, etc. can be calculated in a different domain and then combined with the energy consumption efficiency of the vehicle  10  as desired. 
         [0046]    In other words, although the control strategy  100  is described herein as an energy domain approach, the energy domain approach could be combined with a time or distance domain approach. 
         [0047]    A distance traveled  105  (such as in kilometers (km) or miles (m)) may be obtained by integrating the vehicle speed  101 . In addition, an energy consumed  107  (such as in kW-hr or gallons of fuel) can be obtained by integrating the power consumption  103  of the vehicle  10  and scaling based on application unit conversions. In the discrete time domain, the integration of the vehicle speed  101  that occurs at integrator block  102  may be represented by equation (1), and the integration of the power consumption  103  that occurs at integrator block  104  may be represented by equation (2). These equations are presented below: 
         [0000]        d   traveled ( k )= d   traveled ( k −1)  v ( k )Δt   (1)
 
         [0000]        E   consumed ( k )= E   consumed ( k −1)+ P ( k )Δ t    (2)
 
         [0000]    where:
       d traveled  is the distance traveled since a previous integrator reset;   E consumed  is the energy consumed since a previous integrator reset;   P is the total power consumption;   v is the vehicle speed;   k is the discrete time index (i.e., an arbitrary value); and   Δt is the sampling time of the control module  28 .       
 
         [0054]    If the energy consumed  107  since a previous integrator reset is greater than or equal to an energy consumed threshold, then the energy consumption efficiency should be updated. The energy consumed threshold may be any threshold, and its actual value could depend on multiple factors including but not limited to the vehicle type. The control strategy  100  ends if the energy consumed threshold has not been exceeded by the energy consumed  107 . In other words, the estimated energy consumption efficiency remains constant and is not updated. 
         [0055]    However, if the control module  28  determines that the energy consumed threshold has been exceeded, the integrator blocks  102  and  104  are both reset by a reset logic block  106 . In other words, the reset logic block  106  resets the integrator blocks  102 ,  104  to zero. By way of one non-limiting embodiment, if the energy consumed exceeds an energy consumption threshold of 10 W-hr, for example, then the energy consumed and the distance threshold is set to zero such that another 10 W-hr must be consumed before the next update. This update is based on the distance traveled during the interval over which that particular 10 W-hr is consumed. 
         [0056]    The control strategy  100  then proceeds to block  108 . At block  108 , a ratio of the distance traveled  105  to the energy consumed  107  is obtained. 
         [0057]    The ratio from block  108  is next filtered at filter block  110 . The filter block  110  filters out noise and selectively adapts an average energy consumption efficiency. In one non-limiting embodiment, for a first order discrete filter, the energy consumption efficiency can be calculated according to the following equation: 
         [0000]    
       
         
           
             
               
                 
                   
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             γ is the learned energy consumption efficiency; 
             γ input  is the energy consumption efficiency of the current interval; 
             E threshold  is the energy consumed threshold; and 
             τ E  is the filter constant of the discrete filter in units of energy (i.e., a filter constant). 
           
         
       
     
         [0063]    The filter block  110  calculates an updated energy consumption efficiency  112 . The updated energy consumption efficiency  112  can be used to control the vehicle  10  at block  114 . For example, the learned energy consumption efficiency can be used to calculate at least one end used feature associated with the vehicle  10 . In one non-limiting embodiment, the end use feature includes a distance to empty estimation. However, other end use features are additionally contemplated, including but not limited to, instantaneous consumption rate display, average consumption rate/efficiency over the trip odometer, running average consumption rate/efficiency for the current key cycle, lifetime running average consumption rate/efficiency, grade estimation, towing load estimation, energy management, adaptive ECO-routing, ECO-coaching, etc. 
         [0064]    In another embodiment, once the updated energy consumption efficiency  112  has been calculated or “learned,” an energy consumption rate associated with the vehicle  10  can also be calculated. The energy consumption rate is calculated by taking the inverse of the energy consumption efficiency. Knowing the energy consumption rate can also be helpful for controlling the vehicle  10  or for estimating various end use features associated with the vehicle  10 . 
         [0065]    Learning the energy consumption efficiency in the energy domain as detailed above provides an unbiased estimate of energy consumption efficiency in which an arbitrary learning rate can be chosen. Thus, the control strategy  100  can be used for all applications regardless of time scale requirements. 
         [0066]      FIG. 3  schematically illustrates a control strategy  200  according to another embodiment of this disclosure. In this embodiment, the control strategy  200  includes learning an energy consumption efficiency of the vehicle  10  in an energy domain at block  202 . For example, the energy consumption efficiency may be learned by preprocessing a time domain input and then intelligently triggering an update of an energy domain filter. 
         [0067]    Next, at block  204 , the vehicle  10  is controlled using the learned energy consumption efficiency. For example, a plurality of end use features may be calculated and displayed to a vehicle driver by using the learned energy consumption efficiency. 
         [0068]    As shown at block  206 , the control strategy  200  can periodically suspend energy consumption efficiency learning under certain conditions. By way of one non-limiting embodiment, the control strategy  200  can be temporarily suspended if the vehicle  10  is driving down a steep grade and it is desired to not have the energy consumption efficiency of the vehicle reflected during such a driving situation. Other conditions may also call for suspending the energy consumption efficiency learning. The control strategy  200  could resume learning at block  208  once normal driving conditions have been resumed. 
         [0069]    Although the different non-limiting embodiments are illustrated as having specific components or steps, the embodiments of this disclosure are not limited to those particular combinations. It is possible to use some of the components or features from any of the non-limiting embodiments in combination with features or components from any of the other non-limiting embodiments. 
         [0070]    It should be understood that like reference numerals identify corresponding or similar elements throughout the several drawings. It should be understood that although a particular component arrangement is disclosed and illustrated in these exemplary embodiments, other arrangements could also benefit from the teachings of this disclosure. 
         [0071]    The foregoing description shall be interpreted as illustrative and not in any limiting sense. A worker of ordinary skill in the art would understand that certain modifications could come within the scope of this disclosure. For these reasons, the following claims should be studied to determine the true scope and content of this disclosure.