International Engine Intellectual Property Company, LLC

Askin Minaz

Ravi Rayala

Robert Manoni

Patrick Yerkes

Rahul Rajampeta Rahul Rajampeta

Manoj Vemuri

Jungme Park

Sriram Jayachandran Raguraman

Grant Davisson

An intelligent charge limit for high voltage battery charging provides increased e-motor regeneration and reduced application of the foundation brake system of an electric vehicle. A plurality of data factors are used to calculate a maximum charge percentage. The maximum charge percentage is used to ensure there is enough charging capacity in a battery to store electrical current generated by an e-motor during downhill travel. The intelligent charge limit also provides an operator of the electric vehicle, the option of changing the charge limit to ensure that the battery receives the maximum electrical current generated by an e-motor, during downhill travel. The operator may also double plug a charging gun to eliminate a state of charge routine from considering offsetting the operator charging percentage by the plurality of data factors.

Intelligent charge limit for high voltage batteries

The Method and Arrangement for Predicting Engine Out Nitrogen Oxides (EONOx) includes training multiple candidate Artificial Neural Network (ANN) architectures using training data, and then selecting an ANN architecture from the candidates using an automated ANN architecture selection algorithm and testing data. An intelligent EONOx prediction or estimation system using the selected ANN architecture then provides an EONOx output variable, which is used along with the output of an EONOx sensor. The system is deployed into the engine controller. The training and testing sets of data include input variables from engine sensors and/or actuators that relate to EONOx, and may be acquired by testing a target engine. Selecting the optimal ANN architecture may be based on Root Mean Squared Error (RMSE) analysis using the automated ANN architecture algorithm and the training set of data.

Method and arrangement for predicting engine out nitrogen oxides (EONOx) using a neural network

The Method and Apparatus of Predicting MAF Sensor Information includes training multiple candidate Artificial Neural Network (ANN) architectures using training data, and then selecting an ANN architecture from the candidates using an automated ANN architecture selection algorithm and testing data. An intelligent engine intake MAF prediction or estimation system using the selected ANN architecture then provides an engine intake Mass Air Flow (MAF) output variable, which is used along with the output of a hot-wire type engine intake MAF sensor. The system is deployed into the engine controller. The training and testing sets of data include input variables from engine sensors and/or actuators that relate to engine intake MAF, and may be acquired by testing a target engine. Selecting the optimal ANN architecture may be based on Root Mean Squared Error (RMSE) analysis using the automated ANN architecture algorithm and the training set of data.

Intelligent mass air flow (MAF) prediction system with neural network

A system and method is provided for estimating engine exhaust nitrogen oxide sensor signal instability in transient conditions, for example when rapid changes occur in driver demanded torque, and for eliminating fluctuations in EONOx sensor signal status, in order to have more robust on-board diagnostics monitoring and exhaust nitrogen oxide control. The system and method predicts EONOx sensor signal instability by comparing a calculated pedal based driver demand torque delta to calculated instability thresholds and instability threshold hysteresis margins, and generates instability flags. The system and method further validates any predicted EONOx sensor signal instability by observation. Upon validation of the predicted EONOx sensor signal instability, the system and method latches the EONOx sensor signal status to a stable value.

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Aurora, IL, United States of America

Askin Minaz