Gm Global Technolgoy Operations, Inc.

Northeastern University

Shiming Duan

Chaitanya Sankavaram

Yao Hu

Christopher Harold Knieper

Paul Edward Krajewski

Yilu Zhang

Shengbing Jiang

Chunhao J Lee

Wen-Chiao Lin

Xiaomeng Peng

Bakhtiar Brian Litkouhi

Ibrahim Haskara

Layne K Wiggins

Steven W Holland

Nicholas W Pinto

Azeem Sarwar

Kausalya Singuru

Xiaoyu Huang

Yasen Hu

Bradley R Frieberg

Mutasim A Salman

Calvin Wang

Farzad Samie

Donald K Grimm

Yue-Yun Wang

Chen-Fang Chang

Chi-Kuan Kao

Xinyu Du

Youssef A Ghoneim

Shih-Ken Chen

SeyedAlireza Kasaiezadeh Mahabadi

Yiran Hu

Stephen Paul Levijoki

Qingrong Zhao

Shifang Li

Zhen J Zhang

Arun Adiththan

Brent S Gagas

Yusheng Zou

Ramesh Sethu

Xiangxing Lu

Dean G Sorrell

David Sun

Todar Kuzmanov

Peter Paul Radecki

Srikanth Reddy Bandameedi

Yingjie Cai

Ming Hsiang Hong

Sambasiva Velama

Xiaodong Jia

A cloud-based platform server includes a memory, a receiving module, and a reporting module. The memory stores a DTC configuration table including DTC rules and a DTC data table. The receiving module: receives DTC configuration information transmitted from a configuration server to the cloud-based platform server and DTC data uploaded from a data server to the cloud-based platform server, where at least some of the DTC data was originally generated by multiple vehicles; based on one or more selected vehicle platforms, parse the DTC configuration information and the DTC data; convert the parsed DTC configuration information to a standardized format and the parsed DTC data having different formats to a standardized format; generate or update the DTC configuration table based on the parsed and converted configuration information; and generate or update the DTC data table based on the parsed and converted DTC data and the DTC rules.

Cloud-based platform server for vehicle DTC data analyzing, reporting and responding

A motor vehicle motion control health monitoring system includes sensors and actuators disposed on the motor vehicle. The sensors measure real-time static and dynamic telemetry data about the motor vehicle, and the actuators alter static and dynamic behavior of the motor vehicle. A control module has a processor, a memory, and input/output (I/O) ports. The processor executes program code portions stored in the memory, the program code portions include: an offline portion that collects telemetry data from the motor vehicle, performs failure analysis on the telemetry data and allocates tasks based on the failure analysis; and an online portion that analyzes the telemetry data for failures within specific sensors, actuators, or functions that utilize systems of sensors and/or actuators. The online portion mitigates deviations in the telemetry data by sending a correction to the one or more sensors, actuators, and/or functions of a motor vehicle motion control system.

Architecture and methodology for vehicle motion control health monitoring and mitigation

A diagnostic system for a vehicle includes a vehicle system configured to operate the vehicle in a normal operating mode and a boosted mode. In the boosted mode, the vehicle system increases at least one of a maximum motor torque, a maximum engine torque, and a maximum battery power available to the vehicle. A wear estimation module is configured to collect wear data associated with a component of the vehicle while being operated in the boosted mode, estimate, based on the collected wear data, wear of the component caused by being operated in the boosted mode, and generate a prediction of a remaining lifetime of the component based on the estimated wear of the component.

Methods and apparatus for propulsion system prognostics due to boosted operation

A system for monitoring a battery assembly includes a processing device configured to receive measurement data from a plurality of battery components, and input the measurement data to a battery model configured to determine parametric data. Based on the battery model, the processing device is configured to acquire the parametric data, extract statistical information based on at least one parameter of each battery component, and input the statistical information to a failure identification module that includes a first classifier configured to determine whether the battery assembly is in a failure condition based on the statistical information. The processing device is configured to output a health indicator having a first value indicating that the battery assembly is healthy based on first classifier determining that the battery assembly is in the healthy condition, and a faulty value based on the first classifier determining that the battery assembly is in a failure condition.

Battery health monitoring and failure identification

A method of using an adaptive fault diagnostic system for motor vehicles is provided. A diagnostic tool collects unlabeled data associated with a motor vehicle, and the unlabeled data is transmitted to a central computer. An initial diagnostic model and labeled training data associated with previously identified failure modes and known health conditions are transmitted to the central computer. The central computer executes a novelty detection technique to determine whether the unlabeled data is novelty data corresponding with a new failure mode or normal data corresponding with one of the previously identified failure modes or known health conditions. The central computer selects an informative sample from the novelty data. A repair technician inputs a label for the informative sample, and the central computer propagates the label from the informative sample to the associated novelty data. The central computer updates the labeled training data to include the labeled novelty data.

Adaptive fault diagnostic system for motor vehicles

A system and method for personalization of adjustable features of a vehicle. The system includes a processor and an actuator. The processor detectors key points of a person from a two-dimensional image and predicts a pose of the person. The processor translates a respective position of the key points from a two-dimensional coordinate system to a three-dimensional coordinate system based in part on the pose and measurements of distances between the key points. The processor determines a baseline configuration of an adjustable feature of the vehicle based in part on measurements between the key points in the three-dimensional coordinate system. The processor causes an actuator to adjust the adjustable feature to conform to the baseline configuration.

Personalization settings based on body measurements

A vehicle including an advanced driver-assistance system (ADAS) and operator-adjustable devices is described. Controlling the vehicle includes identifying a vehicle operator, and capturing a plurality of operator-selectable settings associated with the plurality of operator-adjustable devices for the vehicle operator. A base profile and a second profile are determined for the vehicle operator based upon the first subset associated with non-autonomous operation of the vehicle and the second subset associated with ADAS, respectively. The plurality of operator-adjustable devices are controlled to the operator-selectable settings associated with the base profile when the vehicle is operating in the non-autonomous mode, and the plurality of operator-adjustable devices are controlled to the operator-selectable settings associated with the second profile when the vehicle is being controlled at least in part by one or more of the subsystems of the ADAS.

Method and apparatus for managing operator-selectable settings on-vehicle

Methods and apparatus are provided for determining an offset detection for a fuel level sensor fault. The method includes receiving an electrical resistance reading from a potentiometer of a fuel level sensor and generating an estimated fuel level based on an established fuel usage table that references the electrical resistance reading. The fuel level sensitivity is calculated based on the change in electrical resistance readings divided by the change in the estimated fuel levels (R/F). The fuel level sensitivity is compared to a predetermined sensitivity curve to determine any necessary offset to the electrical resistance reading. Finally, the fuel usage table is updated with the offset to the electrical resistance reading.

Offset detection for fuel level sensor fault

A fault diagnostic system includes: memory including a fault model, the fault model including: a plurality of failure modes of a vehicle; and symptoms respectively associated with each of the failure modes; an updating module configured to: based on a data set, determine a new failure mode that is not already included in the fault model and new symptoms indicative of the occurrence of the new failure mode; modify the fault model by: adding the new failure mode to the fault model; and adding the new symptoms to the fault model in association with the new failure mode; and re-save the fault model in the memory.

Fault model augmentation systems and methods

A method of diagnosing a leak in a coolant system of an automobile includes repeatedly measuring the coolant level within the coolant system at a pre-determined time interval, calculating a short term leak rate, wherein the short term leak rate is the rate of coolant leakage over a first pre-determined length of time, calculating a long term leak rate, wherein the long term leak rate is the rate of coolant leakage over a second pre-determined length of time, further wherein the second pre-determined length of time is longer than the first pre-determined length of time, identifying a coolant system leakage state based on a current coolant level within the coolant system, the short term leak rate, and the long term leak rate, and providing notification of the coolant system leakage state to an operator of the vehicle.

Coolant leak diagnosis

A method of, and a system for, controlling and predicting the health of a torque converter clutch control system is provided. The method includes determining, via a controller, rotational input and output speeds of the torque converter and a torque converter clutch slip. The method also includes determining, via the controller, whether a set of predetermined conditions are met for predicting the health of the torque converter clutch control system. The method includes gathering a plurality of initial features of the vehicle propulsion system, determining statistical information about the plurality of initial features, and selecting at least one feature of the vehicle propulsion system based on the statistical information. Furthermore, the method includes classifying the health of the torque converter clutch control system based on the selected feature or features. In some forms, principal component analysis is used to select the feature or features used for classification.

Torque converter clutch control system health estimation

A system for monitoring operation of a vehicle includes a processing device including an interface configured to receive measurement data from sensing devices configured to measure parameters of a vehicle system. The processing device is configured to receive measurement data from each of the plurality of sensing devices, and in response to detection of a malfunction in the vehicle, input at least a subset of the measurement data to a machine learning classifier associated with a vehicle subsystem, the classifier configured to define a class associated with normal operation of the vehicle subsystem. The processing device is also configured to determine whether the subset of the measurement data belongs to the class, and based on at least a selected amount of the subset of the measurement data being outside of the class, output a fault indication, the fault indication identifying the vehicle subsystem as having a contribution to the malfunction.

Monitoring and diagnosing vehicle system problems using machine learning classifiers

A method of identifying air flow faults within a cooling system of an automobile comprises measuring the temperature of coolant entering a heat exchanger for the cooling system, measuring the temperature of coolant leaving the heat exchanger, and measuring the temperature of ambient air that is flowing into the heat exchanger, calculating Actual Delta T by subtracting the temperature of coolant leaving the heat exchanger from the temperature of coolant entering the heat exchanger, calculating Expected Delta T, wherein Expected Delta T is a pre-determined value of an expected difference between the temperature of the coolant entering the heat exchanger and the temperature of the coolant leaving the heat exchanger, calculating Effective Delta T by subtracting Expected Delta T from Actual Delta T, and identifying a fault in the air flow through the heat exchanger based on the value of Effective Delta T.

Cooling system air flow diagnosis

A scheduling controller in communication with a plurality of autonomous vehicles is described, and includes an operator request compiler, a fleet state-of-health database, an environmental conditions compiler and a fleet scheduling controller. The fleet scheduling controller is configured to deploy the autonomous vehicles based upon inputs from the operator request compiler, the fleet state-of-health database and the environmental conditions compiler. A process for coordinating a fleet of autonomous vehicles includes determining states of health for the autonomous vehicles, and determining a desired autonomous vehicle use requirement from each of a plurality of operators that are associated with the autonomous vehicles. A usage schedule for each of the autonomous vehicles is determined based upon the states of health and the desired autonomous vehicle use requirements from the operators. The autonomous vehicles are deployed based upon the usage schedule.

Method and apparatus for coordinating deployment of a fleet of autonomous vehicles

A vehicle including a fluidic subsystem composed of an electric motor, a motor driver and a fluidic pump that is disposed in a fluidic circuit of the vehicle is described. A controller includes an instruction set that is executable to determine operating parameters associated with the fluidic subsystem, and determine a plurality of power efficiency parameters for the fluidic subsystem based upon the operating parameters. The power efficiency parameters include a hydraulic power efficiency, an electro-mechanical power efficiency and an electric power efficiency. The controller can determine a state of health for the fluidic subsystem based upon the power efficiency parameters, and detect a fault in the fluidic subsystem when the state of health is less than a threshold state of health. The fault can be communicated to a vehicle operator.

Method and apparatus to monitor an on-vehicle fluidic subsystem

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Ann Arbor, MI, United States of America

Shiming Duan