Aditya Mandal

US Patent:

20230115272, Apr 13, 2023

Filed:

Jul 8, 2022

Appl. No.:

17/860838

Inventors:

- San Jose CA, US
Elliott A. ORTIZ-SOTO - San Jose CA, US
Shikui Kevin CHEN - Leander TX, US
Li-Chun CHIEN - Milpitas CA, US
Aditya MANDAL - San Jose CA, US

International Classification:

F02D 41/22
F01L 13/00
F02D 41/00
G01M 15/11
G01M 15/05
G05B 13/02

Abstract:

A classifier capable of predicting if cylinder valves of an engine commanded to activate or deactivate failed to activate or deactivate respectively. In various embodiments, the classifier can be binary or multi-class Logistic Regression, or a Multi-Layer Perceptron (MLP) classifier. The variable displacement engine can operate in cooperation with a variable displacement engine using cylinder deactivation (CDA) or skip fire, including dynamic skip fire and/or multi-level skip fire.

US Patent:

20210003088, Jan 7, 2021

Filed:

Sep 21, 2020

Appl. No.:

17/026706

Inventors:

- San Jose CA, US
Aditya MANDAL - San Jose CA, US
Li-Chun CHIEN - Milpitas CA, US
Elliott ORTIZ-SOTO - San Jose CA, US

International Classification:

F02D 41/14
F01N 13/10
F02D 41/00
F02D 41/22
F02B 5/02
F02B 9/04
G06N 20/00
G06N 5/04

Abstract:

Using machine learning for cylinder misfire detection in a dynamic firing level modulation controlled internal combustion engine is described. In a classification embodiment, cylinder misfires are differentiated from intentional skips based on a measured exhaust manifold pressure. In a regressive model embodiment, the measured exhaust manifold pressure is compared to a predicted exhaust manifold pressure generated by neural network in response to one or more inputs indicative of the operation of the vehicle. Based on the comparison, a prediction is made if a misfire has occurred or not. In yet other alternative embodiment, angular crank acceleration is used as well for misfire detection.

US Patent:

20190145859, May 16, 2019

Filed:

Nov 5, 2018

Appl. No.:

16/180703

Inventors:

- San Jose CA, US
Aditya MANDAL - San Jose CA, US
Li-Chun CHIEN - Milpitas CA, US
Elliot ORTIZ-SOTO - San Jose CA, US

International Classification:

G01M 15/11
G06N 3/08
G06N 20/00
G06N 5/04
F02D 35/02

Abstract:

Using machine learning for misfire detection in a Dynamic firing level modulation controlled internal combustion engine is described. A neural network is used to calculate expected crank acceleration from various inputs, including the dynamically defined cylinder skip fire sequence. The output of the neural network is then compared to a signal indicative of the measured crank acceleration. Based the comparison, a prediction is made if a misfire has occurred or not. In alternative embodiment, the neural network is expanded to include the measured crank acceleration as an additional input. With the latter embodiment, the neural network is arranged to directly predict misfire events.