Patent Publication Number: US-11655744-B2

Title: Predictive ammonia release control

Description:
BACKGROUND 
     Technical Field 
     The present disclosure generally relates to vehicle-exhaust-management systems, and more particularly, to predictive-ammonia-release control systems. 
     Description of the Related Art 
     Regulatory air pollution limits for diesel engines have caused heavy-duty diesel truck manufacturers to adopt engine aftertreatment systems for treating diesel exhaust gases before release into the atmosphere. An aftertreatment system can include a plurality of catalytic units to reduce pollutants, including particulate soot (unburned hydrocarbons) and nitrogen oxide (NOx). One component of an aftertreatment system may be a selective catalytic reduction system to reduce the release of NOx. The function of the selective catalytic reduction system is to convert NOx species into nitrogen (N 2 ) and water through chemical reduction with a reductant species. The reductant species is usually ammonia. Ammonia is generated upon decomposition of urea, which is dosed as a solution via a diesel exhaust fluid doser. 
     In some systems and situations, ammonia passes through the selective catalytic reduction system un-reacted, which is often referred to as ammonia slip. Ammonia slip occurs when ammonia is over-injected into the gas stream from the source of diesel exhaust fluid or when temperatures are too low for ammonia to react, or the catalyst has degraded. Some engine pollutant sensors sense this ammonia slip as an increased release of NOx. The selective catalytic reduction system may take further actions to reduce this interpreted release of NOx, which can result in decreased engine performance and efficiency. It is with respect to these and other considerations that the embodiments described herein have been made. 
     BRIEF SUMMARY 
     Embodiments are generally directed to system and methods of predicting the uncontrolled release of ammonia from an engine and employing countermeasures to reduce such release of ammonia. Briefly, the uncontrolled ammonia release is predicted based on an anticipated or future event associated with the vehicle, such as the vehicle is approaching a hill or other type of topography. One or more countermeasures, such as downshifting or changing engine speed, are employed prior to the event to reduce the uncontrolled release of ammonia. 
     A method of controlling uncontrolled release of ammonia from an engine of a vehicle may be summarized as including determining, by at least one processor, an estimated status of the engine prior to an event; generating, by the at least one processor, a predictive model of uncontrolled ammonia release for the estimated status; selecting, by the at least one processor, at least one engine-related countermeasure based on the predictive model; and employing the selected at least one engine-related countermeasure. 
     The method may further include determining, by the at least one processor and prior to selecting the at least one engine-related countermeasure, that the predictive model satisfies at least one threshold condition. 
     The method may further include determining, by the at least one processor and prior to selecting the at least one engine-related countermeasure, a criticality of uncontrolled ammonia release based on the predictive model; and selecting, by the at least one processor, the at least one engine-related countermeasure based on the determined criticality of uncontrolled ammonia release. 
     Determining the criticality of uncontrolled ammonia release may further include determining, by the at least one processor, a change in enthalpy of the engine from a current status of the engine to the estimated status. 
     The method may further include modifying, by the at least one processor, the predictive model based on the selected countermeasure; and selecting, by the at least one processor, at least one different countermeasure based on the modified predictive model. 
     Determining the estimated status on the engine prior to the event may include determining, by the at least one processor, a current location of the vehicle; determining, by the at least one processor, a route of travel of the vehicle from the current location based on a road map; identifying, by the at least one processor, a hill along the route of travel based on a topography map; and determining, by the at least one processor, the estimated status on the engine based on one or more characteristics of the identified hill. 
     Selecting at least one engine-related countermeasure may include at least one of: selecting, by the at least one processor, an increase to a current speed of the engine; selecting, by the at least one processor, an increase in temperature of combustion of the engine; or selecting, by the at least one processor, a downshift operation from a current gear selection of the vehicle. 
     A predictive-ammonia-release control system for a vehicle may be summarized as including a memory that stores computer instructions; and at least one processor that executes the computer instructions to: determine an estimated status of an engine of the vehicle prior to an event; generate a predictive model of uncontrolled ammonia release for the estimated status; select at least one engine-related countermeasure based on the predictive model; and employ the selected at least one engine-related countermeasure. 
     The at least one processor may further execute the computer instructions to: determine, prior to selecting the at least one engine-related countermeasure, that the predictive model satisfies at least one threshold condition. 
     The at least one processor may further execute the computer instructions to: determine, prior to selecting the at least one engine-related countermeasure, a criticality of uncontrolled ammonia release based on the predictive model; and select the at least one engine-related countermeasure based on the determined criticality of uncontrolled ammonia release. 
     The at least one processor may determine the criticality of uncontrolled ammonia release by further executing the computer instructions to: determine a change in enthalpy of the engine exhaust gas from a current status of the engine to the estimated status. 
     The at least one processor may further execute the computer instructions to: modify the predictive model based on the selected countermeasure; and select at least one different countermeasure based on the modified predictive model. 
     The at least one processor may determine the estimated status on the engine prior to the event by further executing the computer instructions to: determine a current location of the vehicle; determine a route of travel of the vehicle from the current location based on a road map; identify a hill along the route of travel based on a topography map; and determine the estimated status on the engine based on one or more characteristics of the identified hill. 
     The at least one processor may select at least one engine-related countermeasure by further executing the computer instructions to: select a change in a current speed of the engine; select an increase in temperature of combustion of the engine; or select a downshift operation from a current gear selection of the vehicle. 
     A non-transitory computer-readable storage medium having stored thereon instructions that when executed by at least one processor, may cause the at least one processor to perform actions, may be summarized as including determining an estimated status of the engine prior to an event; generating a predictive model of uncontrolled ammonia release for the estimated status; selecting at least one engine-related countermeasure based on the predictive model; and employing the selected at least one engine-related countermeasure. 
     Execution of the instructions by the at least one processor, may cause the at least one processor to perform further actions, the further actions including determining, prior to selecting the at least one engine-related countermeasure, that the predictive model satisfies at least one threshold condition. 
     Execution of the instructions by the at least one processor, may cause the at least one processor to perform further actions, the further actions including determining, prior to selecting the at least one engine-related countermeasure, a criticality of uncontrolled ammonia release based on the predictive model; and selecting the at least one engine-related countermeasure based on the determined criticality of uncontrolled ammonia release. 
     Execution of the instructions by the at least one processor, may cause the at least one processor to perform further actions, the further actions including modifying, by the at least one processor, the predictive model based on the selected countermeasure; and selecting at least one different countermeasure based on the modified predictive model. 
     Determining the estimated status on the engine prior to the event may include determining a current location of the vehicle; determining a route of travel of the vehicle from the current location based on a road map; identifying a hill along the route of travel based on a topography map; and determining the estimated status on the engine based on one or more characteristics of the identified hill. 
     Selecting at least one engine-related countermeasure may include at least one of: selecting a change in a current speed of the engine; selecting, by the at least one processor, an increase in temperature of combustion of the engine; or selecting, by the at least one processor, a downshift operation from a current gear selection of the vehicle. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
       Non-limiting and non-exhaustive embodiments are described with reference to the following drawings. In the drawings, like reference numerals refer to like parts throughout the various figures unless otherwise specified. 
       For a better understanding of the present invention, reference will be made to the following Detailed Description, which is to be read in association with the accompanying drawings: 
         FIG.  1    is a schematic block diagram of a vehicle employing one embodiment of a predictive ammonia release control system in accordance with embodiments described herein; 
         FIG.  2    is a schematic block diagram of a predictive-ammonia-release control system, in accordance with embodiments described herein; 
         FIGS.  3 A- 3 B  are example graphs of results from a ramped mode cycle test of a vehicle engine, in accordance with embodiments described herein; 
         FIGS.  4  and  5    are context diagrams of a predictive-ammonia-release control system, in accordance with embodiments described herein; and 
         FIG.  6    is a flow diagram for a method of operating a predictive-ammonia-release control system, in accordance with embodiments described herein. 
     
    
    
     DETAILED DESCRIPTION 
     The following description, along with the accompanying drawings, sets forth certain specific details in order to provide a thorough understanding of various disclosed embodiments. However, one skilled in the relevant art will recognize that the disclosed embodiments may be practiced in various combinations, without one or more of these specific details, or with other methods, components, devices, materials, etc. In other instances, well-known structures or components that are associated with the environment of the present disclosure, including but not limited to the communication systems and networks, have not been shown or described in order to avoid unnecessarily obscuring descriptions of the embodiments. Additionally, the various embodiments may be methods, systems, media, or devices. Accordingly, the various embodiments may be entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. 
     Throughout the specification, claims, and drawings, the following terms take the meaning explicitly associated herein, unless the context clearly dictates otherwise. The term “herein” refers to the specification, claims, and drawings associated with the current application. The phrases “in one embodiment,” “in another embodiment,” “in various embodiments,” “in some embodiments,” “in other embodiments,” and other variations thereof refer to one or more features, structures, functions, limitations, or characteristics of the present disclosure, and are not limited to the same or different embodiments unless the context clearly dictates otherwise. As used herein, the term “or” is an inclusive “or” operator, and is equivalent to the phrases “A or B, or both” or “A or B or C, or any combination thereof,” and lists with additional elements are similarly treated. The term “based on” is not exclusive and allows for being based on additional features, functions, aspects, or limitations not described, unless the context clearly dictates otherwise. In addition, throughout the specification, the meaning of “a,” “an,” and “the” include singular and plural references. 
       FIG.  1    is a schematic block diagram of a vehicle  100 , such as a Class 8 tractor or other vehicle, employing one embodiment of a predictive-ammonia-release control system, in accordance with embodiments described herein. The vehicle  100  includes a powertrain system  102 . In the embodiment shown in  FIG.  1   , the powertrain system  102  includes an engine  104  (e.g., internal combustion engine), a transmission  106 , and a clutch assembly  108 . The transmission  106  may be a manual transmission, an automated manual transmission, or an automatic transmission that includes multiple forward gears, neutral and a reverse gear operatively connected to an output shaft  110 . The clutch assembly  108  may be positioned between the engine  104  and the transmission  106  to selectively engage/disengage the engine from the transmission. The clutch assembly  108  may be actuated manually, pneumatically, hydraulically, electrically, or via any other suitable mechanism. In operation, the engine  104  receives fuel from a fuel source  112  and converts the energy of the fuel into output torque. The output torque of the engine  104  is converted via the transmission  106  into rotation of the output shaft  110 . 
     The vehicle  100  also includes at least two axles such as a steer axle  114  and at least one drive axle, such as axles  116  and  118 . The output shaft  110  of the transmission  106 , which may include a vehicle drive shaft  120 , is drivingly coupled to the drive axles  116  and  118  for transmitting the output torque generated by the engine  104  to the drive axles  116  and  118 . The steer axle  114  is operatively coupled to a power steering system  122 . The power steering system  122  may be powered hydraulically by an engine-mounted pump as shown in  FIG.  1    or an electrical pump, or the power steering system may be fully electrical with no direct connection to the engine  104 . In at least one embodiment, the power steering system  122  includes an electrically driven steering pump or it may be directly driven with no pump. The steer axle  114  supports corresponding front wheels  124  and the drive axles  116  and  118  support corresponding rear wheels  126 , each of the wheels having service brake components  128 . In some embodiments, the service brake components  128  include air brake components of an air brake system  130 , such as an electrically driven compressor, compressed air supply/return lines, brake chambers, etc. In some embodiments, the brake system may include hydraulic brakes. In other some embodiments, the brake system may utilize electrical air compressors that are not connected to the engine  104 , or brakes that are directly electrically actuated without a pneumatic system. The service brake components  128  may also include wheel speed sensors, electronically controlled pressure valves, and the like, to effect control of the vehicle braking system. 
     The vehicle  100  may further include a cab mounted operator interface, such as a control console  132 , which may include any of a number of output devices  134 , such as lights, graphical displays, buzzers, speakers, gages, and the like, and various input devices  136 , such as digital inputs, touchscreens, toggle switches, push button switches, potentiometers, or the like. The control console  132  may include multiple user interfaces, such as a first user interface in a front portion of the cab and a second user interface in a sleeper portion of the cab. 
     In some embodiments, the vehicle  100  may further include cab or sleeper mounted electrical systems  138  including an infotainment system  140 , a heater and A/C system  142  (e.g., heater, A/C system, auxiliary A/C system), other appliances  144 , such as a microwave, a coffee maker, television, electrical outlets for laptops, USB inputs, etc. At least some of these electrical systems may be referred to as “house loads.” In at least some embodiments, the vehicle  100  may include a navigational device having GPS or other location capability, CD/DVD or other audio/visual functionality. 
     The vehicle  100  may include one or more wired or wireless communications systems  149 , including radio frequency (RF) or infrared (IR) based communication links. The communications capabilities may include but are not limited to Universal Serial Bus (USB), 802.x (e.g., 802.11, 802.15, 802.16, etc.), cellular, dedicated short-range communications (DSRC), Bluetooth®/nearfield protocols, among others. 
     The vehicle also includes a control system  146 . In various embodiments, the control system  146  includes a controller module or controller  148  and an ammonia controller  150 . The controller module  148  may control various aspects of the vehicle  100 , including, but not limited to, the engine  104 , the transmission  106 , the power steering system  122 , the alternator  105 , the communications system  106 , the control console  132 , the electrical systems  138 , the brake system  130 , etc. 
     The ammonia controller  150  employs embodiments described herein to predict ammonia release and to employ one or more countermeasure to reduce the actual amount of ammonia release. Briefly, the ammonia controller  150  identifies an upcoming segment of road and its terrain or topography. The ammonia controller  150  predicts an amount of stored ammonia and a predicted amount of enthalpy change that are expected to occur during the upcoming road segment. The ammonia control then predicts the ammonia release and selects or employs one or more countermeasures. In various embodiments, the ammonia controller  150  generates one or more models to represent the predicted ammonia release. 
     In some embodiments, the ammonia controller  150  iteratively selects different countermeasures and models their impact on the predicted ammonia release to determine the most effective countermeasure (e.g., one or more countermeasures that reduce the predicted ammonia release the most compared to one or more other countermeasures). Once the most effective countermeasures are determined, the ammonia controller  150  may employ the countermeasures, which may include controlling one or more systems or components of the vehicle or presenting a notification to the driver to take action, as discussed further below. 
     In at least one embodiment, the modeling of predicted ammonia release may be determined by a server or other computing system that is separate or remote from the vehicle. In this way, the remote computing device can determine which countermeasures are most effective for different scenarios of predicted ammonia release. This information can then be provided to the vehicle for storage such that the ammonia controller  150  can identify the upcoming road segment and select the appropriate countermeasure based on the stored information. 
     The vehicle  100  may also include conventional operator control inputs, such as a clutch pedal  152  (in some manual systems), an ignition or start switch  154 , a throttle or an accelerator pedal  156 , a service brake pedal  158 , a parking brake  160  and a steering wheel  162  to effect turning of the front wheels  124  and/or wheels on other axles of the vehicle  100 . 
     While the vehicle  100  of  FIG.  1    may employ a powertrain utilizing an internal combustion engine as the vehicle motive force, the vehicle represents only one of the many possible applications for the systems and methods of the present disclosure. It should be appreciated that aspects of the present disclosure transcend any particular type of land or marine vehicle and any type of powertrain. 
       FIG.  2    shows one non-limiting example of the ammonia controller  150  formed in accordance with aspects of the present disclosure. In various embodiments, the ammonia controller  150  may also be referred to as a predictive-ammonia-release controller or a predictive-ammonia-release control system. The ammonia controller  150  may be connected in electrical communication with a plurality of data sources  200   a - 200   n  (generally, data sources  200 ). The data sources  200  may include, but are not limited to, navigation equipment, on-board sensors, on-board state estimators, etc. It will be appreciated that the ammonia controller  150  can be connected directly (wired or wirelessly) to the plurality of data sources  200  or indirectly via any suitable interface, such as a CAN interface  202 . Those skilled in the art and others will recognize that the CAN  202  may be implemented using any number of different communication protocols such as, but not limited to, Society of Automotive Engineers (“SAE”) J1587, SAE J1922, SAE J1939, SAE J1708, and combinations thereof. 
     The ammonia controller  150  may also communicate with other electronic components of the vehicle  100  via the CAN  202  for collecting data from other electronic components to be utilized by the ammonia controller  150 , and as such, can also be considered in at least some embodiments as data sources  200 . For example, the ammonia controller  150  may receive data from one or more other controllers  218 , such as an engine controller, a transmission controller, an air suspension system controller, etc. In operation, as will be described in more detail below, the ammonia controller  150  receives signals from the data sources  200 , processes such signals and others, and depending on the processed signals, transmits suitable control signals for operating the engine  104 , the alternator  105 , the power system  146 , or other components of the vehicle  100 . The ammonia controller  150  may be a standalone controller or may be part of one or more other controllers (e.g., vehicle electronic control unit (VECU)) of the vehicle  100 . Generally, the predictive-ammonia-release control system may be implemented in any local or remote controller(s) operative to provide the functionality described herein. 
     In at least some embodiments, the ammonia controller  150  may contain logic rules implemented in a variety of combinations of hardware circuitry components and programmed processors to effect control of various systems of the vehicle  100 . To that end, as further illustrated in  FIG.  2   , one suitable embodiment of the ammonia controller  150  includes a nontransitory memory  204 , a processor  206 , and a predictive adaptive ammonia control module  208  for providing functionality of the ammonia controller  150 . 
     The memory  204  may include computer readable storage media in read-only memory (ROM)  210  and random-access memory (RAM)  212 , for example. The computer-readable storage media may be implemented using any of a number of 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, including data  214  (e.g., programmable parameters or various models as described herein). 
     The ammonia controller  150  also includes one or more input/output devices or components  216  that enable the controller to communicate with one or more local or remote devices via wired or wireless communication. In at least some embodiments, the ammonia controller  150  may include additional components including but not limited to a high speed clock, analog to digital (A/D) and digital to analog (D/A) circuitry, other input/output circuitry and devices (I/O), and appropriate signal conditioning and buffer circuitry. 
     As used herein, the term processor is not limited to integrated circuits referred to in the art as a computer, but broadly refers to one or more of a microcontroller, a microcomputer, a microprocessor, a central processing unit (CPU), a graphics processing unit (GPU), a programmable logic controller, an application specific integrated circuit, other programmable circuits, combinations of the above, among others. In at least one embodiment, the processor  206  executes instructions stored in memory  204 , such as predictive adaptive ammonia control module  208 , to implement the functionality described in the present disclosure. 
     The predictive adaptive ammonia control module  208  may include a set of control algorithms, including program instructions, selectable parameters, and calibrations stored in one of the storage media and executed to provide functions described herein. Information transfer to and from the module  208  may be accomplished by way of a direct connection, a local area network bus, a serial peripheral interface bus, wired or wireless interfaces, etc. The algorithms may be executed during preset loop cycles such that each algorithm is executed at least once each loop cycle. Algorithms stored in the non-volatile memory devices may be executed by the processor  206  to monitor inputs from the sensing devices and other data transmitting devices or polls such devices for data to be used therein. Loop cycles may be executed at regular intervals during ongoing operation of the vehicle  100 . Alternatively or additionally, algorithms may be executed in response to the occurrence of one or more events. 
     The processor  206  communicates with various data sources  200  directly or indirectly via the input/output (I/O) interface  216  and suitable communication links. The interface  216  may be implemented as a one or more integrated interfaces that provide various raw data or signal conditioning, processing, and/or conversion, short-circuit protection, and/or the like. Additionally or alternatively, one or more dedicated hardware or firmware chips may be used to condition and process particular signals before being supplied to the processor  206 . In at least some embodiments, the signals transmitted from the interface  216  may be suitable digital or analog signals. 
     The ammonia controller  150  may be a separate controller that implements the ammonia release prediction and countermeasure functionality described herein. However, it should be appreciated that the ammonia controller  150  may be a controller module, which could be software embedded within an existing on-board controller, such as the engine controller, a general purpose controller, other vehicle system controllers, etc. 
     As briefly described above, the data sources  200  can include but are not limited to on-board sensors, navigation/GPS devices, data stores, remote servers, etc. The data supplied from these data sources  200  and others may generally or specifically relate to NOx sensor readings, current vehicle location, predictive movement data, temperature data, and engine operating parameters, among others. 
       FIGS.  3 A- 3 B  are example graphs of results from a ramped mode cycle test of a vehicle engine, in accordance with embodiments described herein. A ramped mode cycle (RMC) test is a multi-mode steady-state engine dynamometer test to measure engine emissions and demonstrate compliance with emission standards. The ramped mode cycle test is performed as a continuous cycle with ramped transitions between the individual modes of engine operation. The RMC test mimics different driving conditions. One such condition may be a vehicle going up a hill. In this condition, the driver often depresses the gas pedal to increase engine power and maintain vehicle speed, which is simulated by the mode changes during the RMC test. 
     Graph  302  in  FIG.  3 A  shows exhaust temperature  304  and exhaust mass flow  306  as a function of time during an RMC test. The different mode changes are visible by the different “steps” in the exhaust mass flow  306 . As the modes change, the exhaust mass flow  306  and exhaust temperature  304  also change. These changes are characterized as a change in enthalpy of the engine exhaust, which may be represented as dH as a function of time (t). Ammonia slip often occurs when there are extreme changes in enthalpy. Peaks  308  and  310  represent two conditions that resulted in such changes in enthalpy during the RMC test. 
     Graph  312  in  FIG.  3 A  shows nitrogen oxide (NOx)  320  and ammonia (NH 3 )  322  sensor readings as a function of time during the RMC test. A NOx benchmark  318  for the RMC test is also shown. As the engine is transitioned between different modes, the NOx  320  and NH 3    322  sensor readings fluctuate. When large changes in enthalpy occur, such as when the RMC test simulates a vehicle going up a hill, the NH 3    322  sensor readings also spikes due to uncontrolled release of NH 3 . For example, when the enthalpy changes at peak  308  and  310 , the NH 3    322  also peaks at  324  and  326 , respectively. Representative arrows  314  and  316  correlate these peaks between graph  302  and  312 . This spike in NH 3  is the uncontrolled release of NH 3 , or ammonia slip. 
     Unfortunately, due to the chemical properties of NH 3 , commercially available NOx sensors do not adequately discriminate between NH 3  and NOx, which results in incorrect NOx readings. As a result, the NOx sensor detects both the presence of NOx and the presence of NH 3 , without being able to distinguish between the two, which results in an incorrectly high amount of NOx being detected during the RMC test. 
     When the controller determines that tailpipe NOx cannot be properly controlled in the high efficiency mode (e.g., as characterized by high relative DEF dosing), the controller will switch the engine into a mode characterized by lower engine-out NOx (e.g., by increased exhaust gas recirculation (EGR), retarded injection timing, or a combination thereof). 
     Unfortunately, if NH 3  is detected by the NOx sensor, then the engine system believes that there is an abundance of NOx, even though there is not. As a result, the engine mode may change to accommodate this higher amount of detected NOx. But because there is actually less NOx than what is detected, the engine mode change may be improper and may result in degraded engine performance and efficiency. 
     As discussed above, graph  312  shows NOx  320  and NH 3    322  sensor readings as a function of time during the RMC test, which is reproduced in  FIG.  3 B . Moreover, graph  312  shows ammonia slip at peaks  324  and  326 . 
     Graph  332  in  FIG.  3 B  shows engine mode  334  and DEF volumetric flow  336  as a function of time during the RMC test. When the NH 3    322  peaks at  324  and  326 , the system senses this as NOx, and thus believes there is an increased amount of NOx. As a result, the engine mode  334  changes from “mode  6 ” to “mode  2 ” in response to the NH 3    322  peak at  324  and from mode  6  to mode  3  in response to the NH 3    322  peak at  326 , which are represented by dashed arrows  328  and  330 , respectively. 
     As mentioned above, and as further discussed below, to reduce ammonia slip caused by changes in engine exhaust gas enthalpy, and thus reduce incorrect engine mode changes, the system generates a predicted amount of stored ammonia and a predicted enthalpy change to predict the amount of uncontrolled ammonia release such that countermeasures can be employed to reduce the enthalpy change and ammonia slip. 
       FIG.  4    is a context diagram of a predictive-ammonia-release control system  400 , in accordance with embodiments described herein. The predictive-ammonia-release control system  400  may be an embodiment of or employed by the ammonia controller  150  in  FIG.  1   . The predictive-ammonia-release control system  400  includes a route management module  402 , an ammonia-storage-model module  412 , and a predictive-adaptive-reactive-ammonia-control module  424 . 
     The route management module  402  includes a predictive route module  404 , a predictive-engine-speed-and-power module  406 , and a predictive-exhaust-mass-flow-and-exhaust-temperature module  408 . The predictive route module  404  generates a route prediction of where the vehicle is travelling. In some embodiments, terrain information is obtained for an upcoming segment of road along which the vehicle is traveling (e.g., the next one kilometer of road). This terrain information may be obtained by comparing the vehicle&#39;s current GPS location and historical GPS locations to a road map to determine on which road and in which direction the vehicle is travelling. A topographical map may also be used to detect changes in terrain along the upcoming road segment. 
     The predictive-engine-speed-and-power module  406  utilizes the terrain information to generate a model that predicts the engine speed and power along the upcoming road segment. In various embodiments, a table or other data structure of terrain changes (e.g., road gradient percentages) may be used to predict the engine speed and power. In some embodiments, vehicle weight, the type of vehicle, type of engine, whether conditions, or other vehicle information may be used to model the predicted engine speed and power. In yet other embodiments, the engine-speed-and-power model may be generated through testing and monitoring engine performance over various different terrain changes. 
     The predictive-exhaust-mass-flow-and-exhaust-temperature module  408  utilizes the engine-speed-and-power model to generate a model that predicts the exhaust mass flow and the exhaust temperature along the upcoming road segment. In various embodiments, a table or other data structure of exhaust mass flow and exhaust temperature relative to engine speed and power may be used to predict the exhaust mass flow and exhaust temperature. In some embodiments, the type of engine, whether conditions, or other information may be used to model the predicted exhaust mass flow and exhaust temperature. In yet other embodiments, the exhaust-mass-flow-and-exhaust temperature model may be generated through testing and monitoring engine performance over various different engine speed and power settings. 
     The ammonia-storage-model module  412  includes a selective-catalytic-reduction-aging model  414 , a target-tailpipe-ammonia-and-nitrogen-oxide module  416 , an engine-out-nitrogen-oxide module  418 , and a diesel-exhaust-fluid-injection-control module  420 . The selective-catalytic-reduction-aging model  414  is a stored model of the aging properties of a selective catalytic reduction system. As a selective catalytic reduction system is used to convert NOx into nitrogen and water, its performance or ability to convert NOx ages or degrades over time. The selective-catalytic-reduction-aging model simulates this degradation. 
     The engine-out-nitrogen-oxide module  418  utilizes the results of the predictive-engine-speed-and-power module  406  and the results of the predictive-exhaust-mass-flow-and-exhaust-temperature module  408  to generate a model of the amount of NOx that will be generated and output by the engine. The engine-out-nitrogen-oxide model may be generated based on known characteristics of the chemical process that creates NOx given engine speed, engine power, exhaust mass flow, and exhaust temperature. Once generated, the engine-out-nitrogen-oxide model can be used to predict an amount of NOx release as a function of various input parameters (e.g., terrain information), which can then be used to select, suggest, or employ one or more countermeasures, as described herein. 
     The target-tailpipe-ammonia-and-nitrogen-oxide module  416  generates estimates the amount of tailpipe ammonia and NOx based on the selective-catalytic-reduction-aging model  414 , the results of the engine-out-nitrogen-oxide module  418 , and results from the diesel-exhaust-fluid-injection-control module  420 . In various embodiments, these estimates are determined based on known characteristics of the chemical process that uses urea to generate ammonia to convert the NOx into nitrogen and water. 
     The diesel-exhaust-fluid-injection-control module  420  controls the selective catalytic reduction system&#39;s use of urea to generate ammonia to convert the NOx into nitrogen. In various embodiments, the diesel-exhaust-fluid-injection-control module  420  predicts an amount of stored ammonia  432  based on the results from the predictive-exhaust-mass-flow-and-exhaust-temperature module  408 , the engine-out-nitrogen-oxide module  418 , and the target-tailpipe-ammonia-and-nitrogen-oxide module  416 . In some embodiments, the predicted stored ammonia  432  is determined based on known characteristics of the chemical process of reducing NOx using ammonia. 
     The predictive-exhaust-mass-flow-and-exhaust-temperature module  408  are also used to determine a predicted enthalpy change  434 . In various embodiments, a table or other data structure of changes in exhaust mass flow and exhaust temperature over time may be used to generate the predicted enthalpy change  434 . In some embodiments, a regenerative powertrain model, current or predicted engine mode, or status and state of the engine, or some combination thereof, may be used to generate the predicted enthalpy change  434 . The predicted enthalpy change  434  may be a single value based on the predicted terrain in the upcoming segment of road or it may be a function of changes in the predicted terrain. 
     The predictive-adaptive-reactive-ammonia-control module  424  includes a predicted-ammonia-release module  428 . The predicted-ammonia-release module  428  generates a model of the ammonia that is predicted to be released during the upcoming segment of road based on the predicted stored ammonia  432  and the predicted enthalpy change  434 . Because the predicted enthalpy change  434  takes into account the predicted exhaust mass flow and the predicted exhaust temperature for a select or identified segment of road, and because the predicted stored ammonia  432  takes into account the DEF control, aging of the selective catalytic reduction system, the engine speed and power, and the exhaust mass flow and temperature, the predicted-ammonia-release model provides representation of how much ammonia is likely to be released as the vehicle traverses the selected or identified road segment. 
     In various embodiments, one or more artificial intelligence or machine learning mechanisms may be employed on known engine and terrain data to generate one or more of the models described herein. 
     The predictive-adaptive-reactive-ammonia-control module  424  also includes an adaptations-and-reactions module  426 . The adaptations-and-reactions module  426  has access to one or more countermeasures that can be employed to reduce the amount of ammonia released. In some embodiments, a table or other data structure may store indications or identifiers of such countermeasures and their predicted behavior (e.g., how the engine would respond to the countermeasure). Examples of such countermeasures may include, but are not limited to, changing a current speed of the engine, increasing the temperature of combustion of the engine, downshifting, adjusting the chassis height over the axle, or modifying other operating parameters or characteristics of the engine that impact the predicted stored ammonia  432  or the predicted enthalpy change  434 . 
     In various embodiments, the adaptations-and-reactions module  426  selects one or more countermeasures based on the predicted ammonia release from the predicted-ammonia-release module  428 . In some embodiments, the adaptations-and-reactions module  426  may also take into account the current engine mode or the current status or state of the engine in selecting the countermeasures. Various different parameters or thresholds may be used to select the countermeasures. For example, if the predicted ammonia release is X and the current exhaust temperature is A, then the selected countermeasure may be M. But if the predicted ammonia release is Y and the current exhaust temperature is A, then the selected countermeasure may be N. Given the dynamic range and type of possible countermeasures, generic variables are used as examples, but one of ordinary skill in the art would understand which and how countermeasures can be selected for different predicted amounts of ammonia release and different engine conditions. 
     In some embodiments, the predicted-ammonia-release module  428  can utilize the selected countermeasures from the adaptations-and-reactions module  426  to update or modify the predicted ammonia release model. In various embodiments, the predicted-ammonia-release module  428  and the adaptations-and-reactions module  426  can utilize a dynamic feedback loop to iteratively determine or modify the predicted ammonia release with the goal of identifying a smallest predicted amount of uncontrolled ammonia release. 
     In at least one embodiment, the results of the adaptations-and-reactions module  426  may be provided to the route management module  402  or the ammonia-storage-model module  412 , or some combination thereof. These modules can use the selected countermeasures to dynamically adjust the various models to update the predicted stored ammonia  432  or the predicted enthalpy change  434 , or both. 
     In various embodiments, an adaptations and actions output  440  may be generated from the results of the predictive-adaptive-reactive-ammonia-control module  424 . The adaptations and actions output  440  may be a list or other data structure of predicted ammonia release and corresponding countermeasures. 
     In some embodiments, the route management module  402 , ammonia-storage-model module  412 , and the predictive-adaptive-reactive-ammonia-control module  424  may be employed by the ammonia controller  150  or other computing system on the vehicle. In this way, the vehicle can dynamically predict ammonia release and enthalpy change to select countermeasures to employ along a route. 
     In other embodiments, the route management module  402 , ammonia-storage-model module  412 , or the predictive-adaptive-reactive-ammonia-control module  424 , or some combination thereof, may be employed by a server or computing system that is remote from the vehicle. In at least one embodiment, it may employ a variety of different possible terrain scenarios to generate separate countermeasure results for each scenario. These results (e.g., the adaptations and reactions output  440 ) may be provided to the vehicle, such that the vehicle can perform a lookup of which countermeasure to employ for the terrain in an upcoming segment of road. In this way, the vehicle can perform faster selections of countermeasures without having to regenerate one or more models. 
       FIG.  5    is a context diagram of additional aspects of a predictive-ammonia-release control system  500 , in accordance with embodiments described herein. 
     The system  500  includes a predicted-ammonia-release module  502  and an adaptations-and-reactions module  522 , which may be embodiments of the predicted-ammonia-release module  428  and the adaptations-and-reactions module  426  in  FIG.  4   . 
     In some embodiments, the predicted-ammonia-release module  502  may employ a criticality state machine  504 . The criticality state machine  504  may determine a criticality associated with the predicted uncontrolled release of ammonia. The criticality may be selected based on the amount of ammonia to be released, the duration of ammonia release, the duration of recovery (e.g., how long is the engine in an engine mode that is incorrect for the actual amount of NOx being generated), or other parameters. 
     In the illustrated example in  FIG.  5   , there are five criticality states, high  506 , medium-high  508 , medium-low  510 , low  512 , and null  514 . High  506  may be characterized as high predicted enthalpy change and high predicted NH 3  storage. Medium-high  508  may be characterized as (1) high predicted enthalpy change and medium predicted NH 3  storage or (2) medium predicted enthalpy change and high predicted NH 3  storage. Medium-Low  510  may be characterized as medium predicted enthalpy change and medium predicted NH 3  storage. Low  512  may be characterized as (1) medium predicted enthalpy change and low predicted NH 3  storage or (2) low predicted enthalpy change and medium predicted NH 3  storage. Null  514  may be characterized as low predicted enthalpy change and low predicted NH 3  storage. The terms low, medium, and high may be threshold amounts or values. In some embodiments, these thresholds may be selected by an administrator. In other embodiments, the thresholds may be determined by a machine learning mechanism that is trained on historical data of uncontrolled ammonia release in response to enthalpy change and stored ammonia. Moreover, the thresholds may change depending on vehicle type, emissions standards, engine setting or performance, or other parameters. 
     The adaptations-and-reactions module  522  can utilize the critically to select one or more countermeasures. The adaptations-and-reactions module  522  may include a vehicle management module  524 , which selects countermeasures and controls one or more features of the engine or vehicle to implement the selected countermeasures. For example, the vehicle management module  524  may control the engine  526  (e.g., modifying the engine speed), adjust the transmission  528  (e.g., downshifting), or modify the aftertreatment controls  432  (e.g., reduce DEF injection rates or reduce exhaust gas recirculation mass flow), etc. One or more of these countermeasures can be employed depending on the criticality. 
     It should be noted that these examples are for illustrative purposes and other criticality measures or countermeasures may be employed. 
     The operation of certain aspects will now be described with respect to  FIG.  6   . In at least one of various embodiments, process  600  described in conjunction with  FIG.  6    may be implemented by or executed via circuitry or on one or more computers, such as ammonia controller  150 . Moreover, the various modules in  FIGS.  4  and  5    may be used to implement one or more aspects of process  600 . 
       FIG.  6    is a flow diagram for a method  600  of operating a predictive-ammonia-release control system, in accordance with embodiments described herein. Process  600  begins, after a start block, at block  602 , where an estimated status of a vehicle engine is determined prior to an event. In some embodiments, the event may be associated with a change or likely change in the performance or utilization of the vehicle engine. For example, the event may be a model of the terrain in an upcoming segment of road. Or the event may be an indication that the vehicle is to pass another vehicle. In at least one embodiment, the event may be a situation that may induce additional load on the engine. 
     In some embodiments, the estimated status may include the predicted engine speed and power, predicted exhaust mass flow and exhaust temperature, or other vehicle states or conditions associated with the event. In other embodiments, the estimated status may include the predicted stored ammonia and the predicted enthalpy change associated with the event. In at least one embodiment, the estimated status is the estimated load on the engine that could result in ammonia slip. 
     Process  600  proceeds to block  604 , where a predictive model of uncontrolled ammonia release is generated for the estimated status, such as discussed above based on predicted stored ammonia and predicted enthalpy change. 
     Process  600  continues at block  606 , where a criticality of the predicted uncontrolled ammonia release is determined. As discussed above, one or more criticality thresholds or indicators may be determined. 
     Process  600  proceeds next to decision block  608 , where a determination is made whether the predictive model satisfies at least one threshold condition. Satisfying the threshold may occur when ammonia slip is predicted and one or more countermeasures should be employed based on the determined criticality of the predicted ammonia release. If the predictive model satisfies a threshold condition, then process  600  flows to block  610 ; otherwise, process  600  loops to block  602  to determine the estimated status of the engine prior to a next event (e.g., another hill or a steeper portion of the current hill). 
     At block  610 , where on or more countermeasures are selected based on the determined criticality, as discussed above. 
     Process  600  continues at block  612 , where the predictive model of uncontrolled ammonia release is modified based on the selected countermeasure. In various embodiments, an effect of the selected countermeasure is determined and employed into generating the predicted ammonia release. For example, if the countermeasure is to downshift, then an estimated increase in revolutions per minute and corresponding engine temperature changes can be determined and taken into account when predicting ammonia release for the event. 
     Process  600  proceeds next to decision block  614  to determine if the modified predictive model satisfies the one or more threshold conditions. In various embodiments, decision block  614  may employ embodiments of decision block  608  to determine if the modified predictive model satisfies a threshold condition. If the modified predictive model satisfies a threshold condition, then process  600  loops to block  610  to select one or more different countermeasures; otherwise, process  600  flows to block  616 . In some embodiments, the modified predictive model may not satisfy a threshold condition when a lowest amount of predicted ammonia release is determined among a plurality of different countermeasure options. 
     At block  616 , the selected countermeasures are employed. In some embodiments, one or more of the selected countermeasures are automatically employed. In other embodiments, a notification, warning, or other information may be presented to the driver of the vehicle to employ one or more of the selected countermeasures. For example, a head unit or other visual or audio interface may display a warning to the driver instructing the driver to downshift prior to the event occurring, e.g., prior to the vehicle reaching a hill. 
     Although process  600  is described as be implemented by a controller or computing device on a vehicle, embodiments are not so limited; rather, in some embodiments, a computing device remote from the vehicle may perform the functions of process  600  and provide the selected countermeasures to the vehicle for implementation. 
     The above description of illustrated embodiments, including what is described in the Abstract, is not intended to be exhaustive or to limit the embodiments to the precise forms disclosed. Although specific embodiments and examples are described herein for illustrative purposes, various equivalent modifications can be made without departing from the spirit and scope of the disclosure, as will be recognized by those skilled in the relevant art. 
     For instance, the foregoing detailed description has set forth various embodiments of the devices and/or processes via the use of block diagrams, schematics, and examples. Insofar as such block diagrams, schematics, and examples contain one or more functions and/or operations, it will be understood by those skilled in the art that each function and/or operation within such block diagrams, flowcharts, or examples can be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or virtually any combination thereof. In one implementation, the present subject matter may be implemented via Application Specific Integrated Circuits (ASICs). However, those skilled in the art will recognize that the embodiments disclosed herein, in whole or in part, can be equivalently implemented in standard integrated circuits, as one or more computer programs executed by one or more computers (e.g., as one or more programs running on one or more computer systems), as one or more programs executed by one or more controllers (e.g., microcontrollers) as one or more programs executed by one or more processors (e.g., microprocessors, central processing units, graphical processing units), as firmware, or as virtually any combination thereof, and that designing the circuitry and/or writing the code for the software and or firmware would be well within the skill of one of ordinary skill in the art in light of the teachings of this disclosure. 
     When logic is implemented as software and stored in memory, logic or information can be stored on any processor-readable medium for use by or in connection with any processor-related system or method. In the context of this disclosure, a memory is a processor-readable medium that is an electronic, magnetic, optical, or other physical device or means that contains or stores a computer and/or processor program. Logic and/or the information can be embodied in any processor-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions associated with logic and/or information. 
     In the context of this specification, a “non-transitory processor-readable medium” can be any element that can store the program associated with logic and/or information for use by or in connection with the instruction execution system, apparatus, and/or device. The processor-readable medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device. More specific examples (a non-exhaustive list) of the computer readable medium would include the following: a portable computer diskette (magnetic, compact flash card, secure digital, or the like), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM, EEPROM, or Flash memory), a portable compact disc read-only memory (CDROM), digital tape, and other non-transitory media. 
     The various embodiments described above can be combined to provide further embodiments. These and other changes can be made to the embodiments in light of the above-detailed description. In general, in the following claims, the terms used should not be construed to limit the claims to the specific embodiments disclosed in the specification and the claims, but should be construed to include all possible embodiments along with the full scope of equivalents to which such claims are entitled. Accordingly, the claims are not limited by the disclosure.