Ford Global Technolgoies, LLC

Ford Motor Company Limited

Gerald Jesion

Lee Albert Feldkamp

Gintaras Vincent Puskorius

Leighton Ira Davis, Jr

Fumin Yuan

James W Butler

James Calvey Carnes

Christine A Gierdzak

A method for air/fuel operation of an engine. The method includes providing a model of the engine. The model represents a relationship between: (1) a signal model LAMBSE, representative of estimated air/fuel ratio of the engine relative to a stoichiometric air/fuel ratio for the engine; and, (2) fuel injected into the cylinder of the engine. Exhaust gas oxygen emission from the engine is measured during operation of such engine. Actual LAMBSE produced by such engine during operation of such engine is produced as a function of such measured oxygen. The actual LAMBSE is compared with the model LAMBSE provided by the model in response to fuel injected into the engine to produce a model error signal. The fuel injected into the engine is adjusted in accordance with the error signal.

Method for canister purge compensation using internal model control

A method for air/fuel operation of an engine. The engine is supplied fuel from both a fuel purging system to purge fuel in a fuel supply and feed such purged fuel to an intake manifold of the engine and a fuel injection system to inject fuel from such fuel supply into a cylinder of such engine. The method includes producing a first air/fuel ratio control signal in accordance with measured exhaust gas oxygen emission from the engine; producing a second air/fuel ratio control signal in accordance with fuel transport delay through the fuel purging system; combining the first and second air/fuel ratio control signals into a composite control signal; and feeding such composite control signal to the fuel injection system. Producing the first air/fuel ratio control signal comprises determining fuel flow rate through the purge system. The purge system includes a valve, such valve passing the fuel in the purging system to the intake manifold at a rate relate to a duty cycle of a control signal fed to such valve and wherein the flow rate is determined in response to the duty cycle the control signal fed to the valve. The purge system includes a hydrocarbon sensor responsive to fuel in the purging system and wherein the first air/fuel ratio control signal is produced in accordance with an output of such sensor. The method includes determining a species of hydrocarbon in the fuel being purged and adjusting the first air/fuel ratio control signal in accordance with the determined species. The species determination comprises determining from the exhaust gas oxygen a deviation of the engine emissions from stoichiometry. The method includes providing a model of the engine. The model represents a relationship between: (1) a signal model LAMBSE, representative of estimated air/fuel ratio of the engine relative to a stoichiometric air/fuel ratio for the engine; and, (2) fuel injected into the cylinder of the engine. Exhaust gas oxygen emission from the engine is measured during operation of such engine. Actual LAMBSE produced by such engine during operation of such engine is produced as a function of such measured oxygen. The actual LAMBSE is compared with the model LAMBSE provided by the model in response to fuel injected into the engine to produce a model error signal. The fuel injected into the engine is adjusted in accordance with the error signal.

Feed forward method for canister purge compensation within engine air/fuel ratio control systems having fuel vapor recovery

A electronic engine control (EEC) module executes a generic neural network processing program to perform one or more neural network control funtions. Each neural network funtion is defined by a unitary data structure which defines the network architecture, including the number of node layers, the number of nodes per layer, and the interconnections between nodes. In addition, the data structure holds weight values which determine the manner in which network signals are combined. The network definition data structures are created by a network training system which utilizes an external training processor which employs gradient methods to derive network weight values in accordance with a cost function which quantitatively defines system objectives and an identification network which is pretrained to provide gradient signals representative the behavior of the physical plant. The training processor executes training cycles asynchronously with the operation of the EEC module in a representative test vehicle.

Generic neural network training and processing system

A method of time-aligning sequential gas concentrations, generated by a non-plug flow real time emissions analyzer, with corresponding plug flow inputs from an emissions source includes selecting a sequence of known plug flow test gas pulses representative of the emissions source, feeding the known plug flow test gas pulses into the analyzer, saving a sequence of blurred test pulses generated by the analyzer, determining a mathematical relationship between the known plug flow test gas pulses and the blurred test pulses, configuring a filter such that the filter corresponds to the mathematical relationship, inputting a subset of the blurred test pulses into the filter, recording a sequence of deconvoluted test pulses generated by the filter, comparing the deconvoluted test pulses with a subset of the known plug flow test gas pulses to generate difference errors, adjusting the filter to minimize the plurality of difference errors, repeating the first inputting step, the first recording step, the comparing step, and the adjusting step to reduce the difference errors, feeding the plug flow inputs into the analyzer, recording the sequential gas concentrations generated by the analyzer, and deconvoluting the sequential gas concentrations.

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Woodhaven, MI, United States of America

Gerald Jesion