DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a controller configured to:” in claims 1-14.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. See at least Fig. 6 and ¶0216-¶0221.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Singh et al. (U.S. PgPub 2024/0369024A1) in view of Borhan et al. (U.S. PgPub 2025/0137414A1). Singh discloses “The present application relates to an apparatus and method for pressurizing and supplying a gaseous fuel to an internal combustion engine, and in particular a gaseous fuel stored as a compressed gas.” (See Fig. 4-5)
Regarding Claim 1, Singh discloses A powertrain system (Fig. 4) for a vehicle (¶0061; “Referring now to FIG. 5, there is shown algorithm 310 that can be programmed in controller 150, and which is a technique for pressurizing booster-fuel supply 30 when internal combustion engine 140 is employed in a vehicle (not shown) to propel the vehicle. “), the powertrain system comprising:
an internal combustion engine (Fig. 4, engine 140) operable on a gaseous fuel (Fig. 4, ¶0060; “gaseous-fuel system 11”; and ¶0045 “The fuel injectors can inject the gaseous fuel during the intake stroke or the compression stroke into respective combustion chambers in engine 140. Preferably, the fuel injectors inject the gaseous fuel later during the compression stroke such that when the ignition source ignites the gaseous fuel it burns in a diffusion combustion mode. The timing of fuel injection can vary for a variety of reasons, such as over the engine load and engine speed map of engine 140 and bulk pressure P.sub.BLK and booster pressure P.sub.BSTR.”));
a gaseous fuel tank system (Fig. 4, 11) having a set of gaseous fuel tanks (Fig. 4, tank 30 and tank 20) for storing pressurized gaseous fuel, the gaseous fuel tank system being configured to be in fluid communication with the engine (Fig. 4, gaseous fuel supplied to engine 140 via fluid communication network);
and a compressor assembly (Fig. 4, compressor 70) for pressurizing gaseous fuel, wherein the powertrain system is operable in a first powertrain operational mode where gaseous fuel is supplied from at least one of the gaseous fuel tanks to the engine in a non-operational mode of the compressor assembly (Fig. 2-3, while accumulator pressure is above threshold and gaseous fuel tank pressure is above threshold, fuel is supplied from at least one of the fuel tanks and compressor 70 is not operated. Additionally, in Fig. 5, when all conditions in step 335 are not met, fuel is supplied from at least one of the fuel tanks, and the compressor is not operated) , and in a second powertrain operational mode where gaseous fuel from at least one of the gaseous fuel tanks is pressurized by the compressor assembly in an operational mode of the compressor assembly and supplied to the engine (Fig. 2-3, while accumulator pressure is equal to or less than a threshold and gaseous fuel tank pressure is equal to or less than the threshold, compressor is operated. Additionally, in Fig. 5, when all conditions in step 335 are met, the compressor is operated);
wherein the powertrain system further comprises a controller (Fig. 4, controller 150) configured to:
determine an operational change of the powertrain system (Fig. 4, depletion of fuel supply 30 pressure and/or bulk fuel tank supply pressure; operational changes including conditions 320/325/330) based on route information (Fig. 4, ¶0061; “Booster-fuel supply 30 can become depleted during operation of internal combustion engine 140 depending upon the conditions under which the engine has been operating. Referring now to FIG. 5, there is shown algorithm 310 that can be programmed in controller 150, and which is a technique for pressurizing booster-fuel supply 30 when internal combustion engine 140 is employed in a vehicle (not shown) to propel the vehicle. “);
and, determine to control the compressor assembly based on the operational change (Fig. 2-3, and 5, determining to control on/off state of compressor based on powertrain system operational changes)
Singh discloses a powertrain fuel system apparatus and control methodology based on current operational changes, including vehicle operating conditions where vehicle speed is greater than zero but zero engine fuel demand also known as an engine ‘motoring’ (¶0062) is occurring, but does not explicitly discloses a controller configured to predict an expected operational change of the powertrain system based on route information describing at least one route segment for an intended route of the vehicle; and, determine to control the compressor assembly based on the predicted expected operational change
Borhan discloses a vehicle powertrain gaseous (¶0007) fuel system (Fig. 2, item 310) apparatus and control algorithm (Fig. 5) based on predicted operational changes (See Fig. 1-2, and Fig. 5 step “determine predictive states of the vehicle”; see at least ¶0076-0079). “determining, by the one or more processors, one or more constraints for the vehicle system; executing, by the one or more processors, a control problem to determine a predictive state of the vehicle system based on the one or more constraints for the vehicle system over the prediction horizon; determining, by the one or more processors, a plurality of control inputs for the vehicle system based on the executed control problem; and commanding, by the one or more processors, the fuel system of the vehicle based on at least one of the determined plurality of control inputs, the command structured to control at least one of a start of injection with at least one cylinder of an engine, a fuel flow rate, or a rail pressure for a common rail coupled to at least one fuel injector of the vehicle.” (¶0008).
Therefore Borhan teaches: a powertrain gaseous fuel system control methodology based on current operational changes but does not explicitly discloses a controller configured to predict an expected operational change of the powertrain system based on route information describing at least one route segment for an intended route of the vehicle; and, determine to control the compressor assembly (Fig. 2, fuel system 310 comprising a fuel pump; see ¶0043) based on the predicted expected operational change in order “to improve the accuracy and performance of the vehicle and/or vehicle system (e.g., reducing emissions, maximizing fuel efficiency, etc.). As a specific example, the iterative tuning of the control-oriented model allows the controller to learn how look ahead information, such as upcoming road grade (e.g., inclines and declines), affect vehicle speed control through actuation of a transmission, a fuel system, and/or an air handling system. For example, the controller may learn that less fuel is required when a decline is upcoming within a predetermined distance.” (¶0020)
It would have been obvious to one with ordinary skill in the art at the time of filing of the invention to have modified the vehicle powertrain gaseous fuel control system of Singh to incorporate the teachings of Borhan to include a powertrain gaseous fuel system control methodology based on current operational changes but does not explicitly discloses a controller configured to predict an expected operational change of the powertrain system based on route information describing at least one route segment for an intended route of the vehicle; and, determine to control the compressor assembly based on the predicted expected operational change in order “to improve the accuracy and performance of the vehicle and/or vehicle system (e.g., reducing emissions, maximizing fuel efficiency, etc.). As a specific example, the iterative tuning of the control-oriented model allows the controller to learn how look ahead information, such as upcoming road grade (e.g., inclines and declines), affect vehicle speed control through actuation of a transmission, a fuel system, and/or an air handling system. For example, the controller may learn that less fuel is required when a decline is upcoming within a predetermined distance , for example when vehicle speed will be greater than zero but zero engine-fuel demand also known as an engine ‘motoring’ will occur.
Regarding Claim 15, Singh discloses A method (Fig. 2-3, and 5) for controlling a compressor assembly (Fig. 4, compressor 70) of a powertrain system (Fig. 4) for a vehicle (¶0061; “Referring now to FIG. 5, there is shown algorithm 310 that can be programmed in controller 150, and which is a technique for pressurizing booster-fuel supply 30 when internal combustion engine 140 is employed in a vehicle (not shown) to propel the vehicle. “), wherein the method comprises: determining, by processing circuitry of a controller (Fig. 4, controller 150), an operational change of the powertrain system Fig. 4, depletion of fuel supply 30 pressure and/or bulk fuel tank supply pressure; operational changes including conditions 320/325/330) based on route information (Fig. 4, ¶0061; “Booster-fuel supply 30 can become depleted during operation of internal combustion engine 140 depending upon the conditions under which the engine has been operating. Referring now to FIG. 5, there is shown algorithm 310 that can be programmed in controller 150, and which is a technique for pressurizing booster-fuel supply 30 when internal combustion engine 140 is employed in a vehicle (not shown) to propel the vehicle. “), and determining, by the processing circuitry of the controller, to control the compressor assembly based on the determined operational change (Fig. 2-3, and 5, determining to control on/off state of compressor based on powertrain system operational changes)
Singh discloses a powertrain fuel system control methodology based on current operational changes, including vehicle operating conditions where vehicle speed is greater than zero but zero engine fuel demand also known as an engine ‘motoring’ is occurring, but does not explicitly disclose the controller configured for predicting, by processing circuitry of a controller, an expected operational change of the powertrain system based on route information describing at least one route segment for an intended route of the vehicle, and determining, by the processing circuitry of the controller, to control the compressor assembly based on the predicted expected operational change .
Borhan discloses a vehicle powertrain gaseous (¶0007) fuel system (Fig. 2, item 310) control algorithm (Fig. 5) based on predicted operational changes (See Fig. 1-2, and Fig. 5 step “determine predictive states of the vehicle”; see at least ¶0076-0079). “determining, by the one or more processors, one or more constraints for the vehicle system; executing, by the one or more processors, a control problem to determine a predictive state of the vehicle system based on the one or more constraints for the vehicle system over the prediction horizon; determining, by the one or more processors, a plurality of control inputs for the vehicle system based on the executed control problem; and commanding, by the one or more processors, the fuel system of the vehicle based on at least one of the determined plurality of control inputs, the command structured to control at least one of a start of injection with at least one cylinder of an engine, a fuel flow rate, or a rail pressure for a common rail coupled to at least one fuel injector of the vehicle.” (¶0008).
Therefore Borhan teaches: a powertrain gaseous fuel system controller configured for predicting, by processing circuitry of a controller, an expected operational change of the powertrain system based on route information describing at least one route segment for an intended route of the vehicle, and determining, by the processing circuitry of the controller, to control the compressor assembly (Fig. 2, fuel system 310 comprising a fuel pump; see ¶0043)based on the predicted expected operational change in order “to improve the accuracy and performance of the vehicle and/or vehicle system (e.g., reducing emissions, maximizing fuel efficiency, etc.). As a specific example, the iterative tuning of the control-oriented model allows the controller to learn how look ahead information, such as upcoming road grade (e.g., inclines and declines), affect vehicle speed control through actuation of a transmission, a fuel system, and/or an air handling system. For example, the controller may learn that less fuel is required when a decline is upcoming within a predetermined distance.” (¶0020)
It would have been obvious to one with ordinary skill in the art at the time of filing of the invention to have modified the vehicle powertrain gaseous fuel control system of Singh to incorporate the teachings of Borhan to include a controller configured for predicting, by processing circuitry of a controller, an expected operational change of the powertrain system based on route information describing at least one route segment for an intended route of the vehicle, and determining, by the processing circuitry of the controller, to control the compressor assembly based on the predicted expected operational change in order “to improve the accuracy and performance of the vehicle and/or vehicle system (e.g., reducing emissions, maximizing fuel efficiency, etc.). As a specific example, the iterative tuning of the control-oriented model allows the controller to learn how look ahead information, such as upcoming road grade (e.g., inclines and declines), affect vehicle speed control through actuation of a transmission, a fuel system, and/or an air handling system. For example, the controller may learn that less fuel is required when a decline is upcoming within a predetermined distance , for example when vehicle speed will be greater than zero but zero engine-fuel demand also known as an engine ‘motoring’ will occur.
Regarding Claim 2, Singh further discloses wherein determine to control the compressor assembly based on the determined operational change comprises determining whether to operate the compressor assembly in the operational mode, switch from the non-operational mode to the operational mode, remain in the non-operational mode, or switch from the operational mode to the non-operational mode (Fig. 2-3, and 5, determining to control on/off or off/on transition states of compressor based on powertrain system operational changes)
Singh does not explicitly disclose determine to control the compressor assembly based on the predicted expected operational change
Borhan teaches determine to control the compressor assembly (Fig. 2, fuel system 310 comprising a fuel pump; see ¶0043) based on the predicted expected operational change (control algorithm (Fig. 5) based on predicted operational changes (See Fig. 1-2, and Fig. 5 step “determine predictive states of the vehicle”; see at least ¶0076-0079). “determining, by the one or more processors, one or more constraints for the vehicle system; executing, by the one or more processors, a control problem to determine a predictive state of the vehicle system based on the one or more constraints for the vehicle system over the prediction horizon; determining, by the one or more processors, a plurality of control inputs for the vehicle system based on the executed control problem; and commanding, by the one or more processors, the fuel system of the vehicle based on at least one of the determined plurality of control inputs, the command structured to control at least one of a start of injection with at least one cylinder of an engine, a fuel flow rate, or a rail pressure for a common rail coupled to at least one fuel injector of the vehicle.” (¶0008) in order “to improve the accuracy and performance of the vehicle and/or vehicle system (e.g., reducing emissions, maximizing fuel efficiency, etc.). As a specific example, the iterative tuning of the control-oriented model allows the controller to learn how look ahead information, such as upcoming road grade (e.g., inclines and declines), affect vehicle speed control through actuation of a transmission, a fuel system, and/or an air handling system. For example, the controller may learn that less fuel is required when a decline is upcoming within a predetermined distance.” (¶0020)
It would have been obvious to one with ordinary skill in the art at the time of filing of the invention to have modified the vehicle powertrain gaseous fuel control system of Singh to incorporate the teachings of Borhan to include determine to control the compressor assembly based on the predicted expected operational change in order “to improve the accuracy and performance of the vehicle and/or vehicle system (e.g., reducing emissions, maximizing fuel efficiency, etc.). As a specific example, the iterative tuning of the control-oriented model allows the controller to learn how look ahead information, such as upcoming road grade (e.g., inclines and declines), affect vehicle speed control through actuation of a transmission, a fuel system, and/or an air handling system. For example, the controller may learn that less fuel is required when a decline is upcoming within a predetermined distance , for example when vehicle speed will be greater than zero but zero engine-fuel demand also known as an engine ‘motoring’ will occur.
Regarding Claim 3, Singh further discloses wherein the controller is configured to determine whether to operate the compressor assembly in the operational mode, switch from the non-operational mode to the operational mode, remain in the non-operational mode, or switch from the operational mode to the non-operational mode by determining whether operating the compressor assembly will increase or decrease powertrain system efficiency (Fig. 2-3 and 5, compressor is selectively operated based on predetermined conditions where it has been determined that the powertrain system efficiency is improved by not operating the compressor and operating the compressor when the available system fuel pressure is below a threshold; see also ¶0002-0003)
Singh does not explicitly disclose wherein the controller is configured to determine whether to operate the compressor assembly by determining whether operating the compressor assembly will increase or decrease powertrain system efficiency in the at least one route segment
Borhan teaches: wherein the controller is configured to determine whether to operate the compressor assembly by determining whether operating the compressor assembly will increase or decrease powertrain system efficiency in the at least one route segment (¶0020 “the controller may learn that less fuel is required when a decline is upcoming within a predetermined distance”), in order “to improve the accuracy and performance of the vehicle and/or vehicle system (e.g., reducing emissions, maximizing fuel efficiency, etc.). As a specific example, the iterative tuning of the control-oriented model allows the controller to learn how look ahead information, such as upcoming road grade (e.g., inclines and declines), affect vehicle speed control through actuation of a transmission, a fuel system, and/or an air handling system. For example, the controller may learn that less fuel is required when a decline is upcoming within a predetermined distance.” ;¶0020. In other words, the model predictive control unit is configured to continuously compare and iteratively improve powertrain system efficiency, for example with respect to fuel system control; based on comparing past and current efficiencies for known upcoming segments of previously travelled routes.)
It would have been obvious to one with ordinary skill in the art at the time of filing of the invention to have modified the vehicle powertrain gaseous fuel control system of Singh to incorporate the teachings of Borhan to include wherein the controller is configured to determine whether to operate the compressor assembly by determining whether operating the compressor assembly will increase or decrease powertrain system efficiency in the at least one route segment in order “to improve the accuracy and performance of the vehicle and/or vehicle system (e.g., reducing emissions, maximizing fuel efficiency, etc.). As a specific example, the iterative tuning of the control-oriented model allows the controller to learn how look ahead information, such as upcoming road grade (e.g., inclines and declines), affect vehicle speed control through actuation of a transmission, a fuel system, and/or an air handling system. For example, the controller may learn that less fuel is required when a decline is upcoming within a predetermined distance.” ;¶0020. In other words, the model predictive control unit is configured to continuously compare and iteratively improve powertrain system efficiency, for example with respect to fuel system control; based on comparing past and current efficiencies for known upcoming segments of previously travelled routes.)
Regarding Claim 4, Singh does not explicitly teach: wherein the controller is configured to determine to control the compressor assembly by determining a first powertrain system efficiency for operating the powertrain system in the first powertrain operational mode in the at least one route segment and a second powertrain system efficiency for operating the powertrain system in the second powertrain operational mode in the at least one route segment, compare the first powertrain system efficiency and the second powertrain system efficiency, and select the operational mode with the determined highest powertrain system efficiency
Borhan teaches: wherein the controller is configured to determine to control the compressor assembly by determining a first powertrain system efficiency for operating the powertrain system in the first powertrain operational mode in the at least one route segment and a second powertrain system efficiency for operating the powertrain system in the second powertrain operational mode in the at least one route segment, compare the first powertrain system efficiency and the second powertrain system efficiency, and select the operational mode with the determined highest powertrain system efficiency in order “to improve the accuracy and performance of the vehicle and/or vehicle system (e.g., reducing emissions, maximizing fuel efficiency, etc.). As a specific example, the iterative tuning of the control-oriented model allows the controller to learn how look ahead information, such as upcoming road grade (e.g., inclines and declines), affect vehicle speed control through actuation of a transmission, a fuel system, and/or an air handling system. For example, the controller may learn that less fuel is required when a decline is upcoming within a predetermined distance.” ;¶0020. In other words, the model predictive control unit is configured to continuously compare and iteratively improve powertrain system efficiency, for example with respect to fuel system control; based on comparing past and current efficiencies for known upcoming segments of previously travelled routes.)
It would have been obvious to one with ordinary skill in the art at the time of filing of the invention to have modified the vehicle powertrain gaseous fuel control system of Singh to incorporate the teachings of Borhan to include wherein the controller is configured to determine to control the compressor assembly by determining a first powertrain system efficiency for operating the powertrain system in the first powertrain operational mode in the at least one route segment and a second powertrain system efficiency for operating the powertrain system in the second powertrain operational mode in the at least one route segment, compare the first powertrain system efficiency and the second powertrain system efficiency, and select the operational mode with the determined highest powertrain system efficiency in order “to improve the accuracy and performance of the vehicle and/or vehicle system (e.g., reducing emissions, maximizing fuel efficiency, etc.). As a specific example, the iterative tuning of the control-oriented model allows the controller to learn how look ahead information, such as upcoming road grade (e.g., inclines and declines), affect vehicle speed control through actuation of a transmission, a fuel system, and/or an air handling system. For example, the controller may learn that less fuel is required when a decline is upcoming within a predetermined distance.” ;¶0020
Regarding Claim 5, Singh does not explicitly disclose: wherein the controller is further configured to predict the expected operational change of the powertrain system based on a previous powertrain system operating profile for the route segment
Borhan teaches: wherein the controller is further configured to predict the expected operational change of the powertrain system based on a previous powertrain system operating profile for the route segment (“the iterative tuning of the control-oriented model allows the controller to learn how look ahead information such as upcoming road grade (e.g., inclines and declines), affect vehicle speed control through actuation of a transmission, a fuel system, and/or an air handling system. For example, the controller may learn that less fuel is required when a decline is upcoming within a predetermined distance.”) in order “to improve the accuracy and performance of the vehicle and/or vehicle system (e.g., reducing emissions, maximizing fuel efficiency, etc.).” (¶0020)
It would have been obvious to one with ordinary skill in the art at the time of filing of the invention to have modified the vehicle powertrain gaseous fuel control system of Singh to incorporate the teachings of Borhan to include wherein the controller is further configured to predict the expected operational change of the powertrain system based on a previous powertrain system operating profile for the route segment in order “to improve the accuracy and performance of the vehicle and/or vehicle system (e.g., reducing emissions, maximizing fuel efficiency, etc.).” (¶0020)
Regarding Claim 6, Singh further discloses wherein the controller is further configured to determine a current powertrain system efficiency and determine to control the compressor assembly based on a comparison with the determined current powertrain system efficiency (Fig. 2-3 and 5, compressor is selectively operated based on predetermined conditions where it has been determined that the powertrain system efficiency is improved by not operating the compressor and operating the compressor when the available system fuel pressure is below a threshold; see also ¶0002-0003)
Regarding Claim 7, Singh does not explicitly disclose: wherein the controller is configured to predict the expected operational change of the powertrain system by determining any one of an expected operational change in engine load, engine torque, engine revolution and engine acceleration
Borhan teaches wherein the controller is configured to predict the expected operational change of the powertrain system by determining any one of an expected operational change in engine load, engine torque, engine revolution and engine acceleration (¶0057; “the cylinder and combustion model circuit 240 may then use look up tables, equations, and/or algorithms (or other processes) within the control-oriented model 228 to determine a predictive state of the vehicle based on predicting that the engine torque will increase by a predefined amount when the acceleration pedal is depressed a predefined amount. The predictive states of the vehicle may include one or more outputs that describe the predictive state of the vehicle (e.g., BSFC, engine torque, NO.sub.x rate, particulate matter emission rate, peak cylinder pressure, charge flow, and EGR flow). “)in the at least one route segment (¶0020; “a decline is upcoming within a predetermined distance”)
It would have been obvious to one with ordinary skill in the art at the time of filing of the invention to have modified the vehicle powertrain gaseous fuel control system of Singh to incorporate the teachings of Borhan to include wherein the controller is configured to predict the expected operational change of the powertrain system by determining any one of an expected operational change in engine load, engine torque, engine revolution and engine acceleration in the at least one route segment in order “to improve the accuracy and performance of the vehicle and/or vehicle system (e.g., reducing emissions, maximizing fuel efficiency, etc.). As a specific example, the iterative tuning of the control-oriented model allows the controller to learn how look ahead information, such as upcoming road grade (e.g., inclines and declines), affect vehicle speed control through actuation of a transmission, a fuel system, and/or an air handling system. For example, the controller may learn that less fuel is required when a decline is upcoming within a predetermined distance.” (¶0020)
Regarding Claim 8, Singh does not explicitly disclose: wherein the predictive information is generated based on historical route data for the intended route for the vehicle
Borhan teaches; wherein the predictive information is generated based on historical route data for the intended route for the vehicle (¶0027, “external static information, where external static information refers to information or data that may vary as a function of position (e.g., the curvature or grade of the road may vary along a route) relative the vehicle 100 but is substantially unchanging with respect to time…. the controller 210 may receive look ahead information from the remote information source 270. “Look ahead” information refers to information/data regarding conditions that may affect operation of the vehicle “ahead” or in front of the vehicle (i.e., upcoming) and, in turn, may include external static and/or dynamic information. Accordingly, the controller 210 may determine or receive upcoming static and/or dynamic look ahead information in addition to existing operating information of the vehicle. In other embodiments, certain look ahead information (e.g., external static information such as maps, road grade, etc.) may be pre-programmed within the controller 210”) in order “to improve the accuracy and performance of the vehicle and/or vehicle system (e.g., reducing emissions, maximizing fuel efficiency, etc.). As a specific example, the iterative tuning of the control-oriented model allows the controller to learn how look ahead information, such as upcoming road grade (e.g., inclines and declines), affect vehicle speed control through actuation of a transmission, a fuel system, and/or an air handling system. For example, the controller may learn that less fuel is required when a decline is upcoming within a predetermined distance.” (¶0020)
It would have been obvious to one with ordinary skill in the art at the time of filing of the invention to have modified the vehicle powertrain gaseous fuel control system of Singh to incorporate the teachings of Borhan to include wherein the predictive information is generated based on historical route data for the intended route for the vehicle in order “to improve the accuracy and performance of the vehicle and/or vehicle system (e.g., reducing emissions, maximizing fuel efficiency, etc.). As a specific example, the iterative tuning of the control-oriented model allows the controller to learn how look ahead information, such as upcoming road grade (e.g., inclines and declines), affect vehicle speed control through actuation of a transmission, a fuel system, and/or an air handling system. For example, the controller may learn that less fuel is required when a decline is upcoming within a predetermined distance.” (¶0020)
Regarding Claim 9, Singh further discloses wherein the controller is further configured to prioritize the operation of the compressor assembly when the change in the operation of the powertrain system is indicative of engine load conditions favoring increased efficiency from pressurized fuel supply (Fig. 2-3, while accumulator pressure is equal to or less than a threshold and gaseous fuel tank pressure is equal to or less than the threshold, compressor is operated. Additionally, in Fig. 5, when all conditions in step 335 are met such as engine motoring where pedal position is at 0% indicating the engine is not under significant load, the compressor is operated)
Singh does not disclose the controller configured to control the gaseous fuel system based on predicted expected changes in operation of the powertrain system.
Borhan teaches the controller configured to control the gaseous fuel system based on predicted expected changes in operation of the powertrain system(See Fig. 1-2, and Fig. 5 step “determine predictive states of the vehicle”; see at least ¶0076-0079). “determining, by the one or more processors, one or more constraints for the vehicle system; executing, by the one or more processors, a control problem to determine a predictive state of the vehicle system based on the one or more constraints for the vehicle system over the prediction horizon; determining, by the one or more processors, a plurality of control inputs for the vehicle system based on the executed control problem; and commanding, by the one or more processors, the fuel system of the vehicle based on at least one of the determined plurality of control inputs, the command structured to control at least one of a start of injection with at least one cylinder of an engine, a fuel flow rate, or a rail pressure for a common rail coupled to at least one fuel injector of the vehicle.” (¶0008) in order “to improve the accuracy and performance of the vehicle and/or vehicle system (e.g., reducing emissions, maximizing fuel efficiency, etc.). As a specific example, the iterative tuning of the control-oriented model allows the controller to learn how look ahead information, such as upcoming road grade (e.g., inclines and declines), affect vehicle speed control through actuation of a transmission, a fuel system, and/or an air handling system. For example, the controller may learn that less fuel is required when a decline is upcoming within a predetermined distance.” (¶0020)
It would have been obvious to one with ordinary skill in the art at the time of filing of the invention to have modified the vehicle powertrain gaseous fuel control system of Singh to incorporate the teachings of Borhan to include the controller configured to control the gaseous fuel system based on predicted expected changes in operation of the powertrain system in order “to improve the accuracy and performance of the vehicle and/or vehicle system (e.g., reducing emissions, maximizing fuel efficiency, etc.). As a specific example, the iterative tuning of the control-oriented model allows the controller to learn how look ahead information, such as upcoming road grade (e.g., inclines and declines), affect vehicle speed control through actuation of a transmission, a fuel system, and/or an air handling system. For example, the controller may learn that less fuel is required when a decline is upcoming within a predetermined distance.” (¶0020)
Regarding Claim 10, Singh further discloses further comprising a buffer tank (¶0039; “pressurized gaseous fuel can be fluidly communicated to accumulator 100 (where it is stored at least temporarily) “)arranged between the engine and the gaseous fuel tanks (Fig. 4; buffer tank 100 arranged between engine 140 and tanks 30 and 20), wherein the buffer tank stores fuel compressed by the compressor assembly for use during conditions where operating the compressor assembly is predicted to decrease engine efficiency (Fig. 2-3, steps 270, fuel from buffer tank 100 used during conditions when compressor is not operating because “The compressor is powered from energy produced by an internal combustion engine and operation of the compressor increases parasitic losses and reduces fuel economy of the engine. “ ¶0002)
Regarding Claim 11, Singh further discloses wherein the controller is configured to deplete the buffer tank during an engine braking event (¶0062, “motoring condition”), and to operate the compressor assembly during the engine braking (Fig. 5, Step 335 all conditions met; proceed to step 340 and operate compressor to recharge/refil the booster/buffer tank) to refill the buffer tank without consuming (¶0062; “zero engine-fuel demand”) additional fuel (Fig. 2-3, while accumulator pressure is equal to or less than a threshold and gaseous fuel tank pressure is equal to or less than the threshold, compressor is operated. Additionally, in Fig. 4, when all conditions in step 335 are met, the compressor is operated)
Singh discloses a powertrain fuel system control methodology based on current operational changes, including vehicle operating conditions where vehicle speed is greater than zero but zero engine fuel demand also known as an engine ‘motoring’ (¶0062) is occurring, but does not explicitly discloses a controller configured to anticipate an engine braking event.
Borhan discloses a vehicle powertrain gaseous (¶0007) fuel system (Fig. 2, item 310) control algorithm (Fig. 5) based on predicted operational changes (See Fig. 1-2, and Fig. 5 step “determine predictive states of the vehicle”; see at least ¶0076-0079). “determining, by the one or more processors, one or more constraints for the vehicle system; executing, by the one or more processors, a control problem to determine a predictive state of the vehicle system based on the one or more constraints for the vehicle system over the prediction horizon; determining, by the one or more processors, a plurality of control inputs for the vehicle system based on the executed control problem; and commanding, by the one or more processors, the fuel system of the vehicle based on at least one of the determined plurality of control inputs, the command structured to control at least one of a start of injection with at least one cylinder of an engine, a fuel flow rate, or a rail pressure for a common rail coupled to at least one fuel injector of the vehicle.” (¶0008) and “the controller utilizes the model predictive controls to improve the accuracy and performance of the vehicle and/or vehicle system (e.g., reducing emissions, maximizing fuel efficiency, etc.). As a specific example, the iterative tuning of the control-oriented model allows the controller to learn how look ahead information, such as upcoming road grade (e.g., inclines and declines), affect vehicle speed control through actuation of a transmission, a fuel system, and/or an air handling system. For example, the controller may learn that less fuel is required when a decline is upcoming within a predetermined distance.” (¶0020)
Therefore Borhan teaches: a controller configured to anticipate an engine braking event.(¶0020) in order “to improve the accuracy and performance of the vehicle and/or vehicle system (e.g., reducing emissions, maximizing fuel efficiency, etc.). As a specific example, the iterative tuning of the control-oriented model allows the controller to learn how look ahead information, such as upcoming road grade (e.g., inclines and declines), affect vehicle speed control through actuation of a transmission, a fuel system, and/or an air handling system. For example, the controller may learn that less fuel is required when a decline is upcoming within a predetermined distance , for example when vehicle speed will be greater than zero but zero engine-fuel demand also known as an engine ‘motoring’ will occur.
It would have been obvious to one with ordinary skill in the art at the time of filing of the invention to have modified the vehicle powertrain gaseous fuel control system of Singh to incorporate the teachings of Borhan to include a controller configured to anticipate an engine braking event.(¶0020) in order “to improve the accuracy and performance of the vehicle and/or vehicle system (e.g., reducing emissions, maximizing fuel efficiency, etc.). As a specific example, the iterative tuning of the control-oriented model allows the controller to learn how look ahead information, such as upcoming road grade (e.g., inclines and declines), affect vehicle speed control through actuation of a transmission, a fuel system, and/or an air handling system. For example, the controller may learn that less fuel is required when a decline is upcoming within a predetermined distance , for example when vehicle speed will be greater than zero but zero engine-fuel demand also known as an engine ‘motoring’ will occur.
Regarding Claim 12, Singh further discloses wherein the gaseous fuel is either a hydrogen-based fuel or a natural gas fuel (¶0011; “The gaseous fuel can be selected from the list containing ammonia, hydrogen, methane, propane, natural gas, and mixtures of these fuels.”)
Regarding Claim 13, Singh further discloses wherein the compressor assembly is configured to at least partly be powered by the internal combustion engine, and/or wherein the compressor assembly is configured to at least partly be powered by an auxiliary power source (¶0040; “compressor 70 can be hydraulically driven with power coming from a hydraulic power offtake (not shown) or electrically driven with power coming from an electric alternator (not shown) that are typically part of engine 140. Alternatively, compressor 70 can be mechanically driven directly from a camshaft (not shown) or an engine power takeoff (not shown) of engine 140.”)
Regarding Claim 14, Singh further discloses A vehicle comprising a powertrain system according to claim 1 (¶0061; “Referring now to FIG. 5, there is shown algorithm 310 that can be programmed in controller 150, and which is a technique for pressurizing booster-fuel supply 30 when internal combustion engine 140 is employed in a vehicle (not shown) to propel the vehicle. “)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Boyer et al. (U.S. Pat. 7367312) discloses “A method for operating a fueling system in a vehicle having an engine capable of burning gaseous fuel supplied from at least a first and second source to a direct fuel injector of the engine, the first source having a higher source pressure than the second source, the method comprises supplying gaseous fuel to the direct injector from at least the first source during a first engine demand; and supplying gaseous fuel to the direct injector from at least the second source during a second engine demand less than said first engine demand.” (abstract)
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/BRIAN R KIRBY/Examiner, Art Unit 3747
/LINDSAY M LOW/Supervisory Patent Examiner, Art Unit 3747
