Gm Global Technolgoy Operations, Inc.

Ibrahim Haskara

Yue-Yun Wang

Frederic Anton Matekunas

Chol-Bum M Kweon

Paul Anthony Battiston

Chen-Fang Chang

Bharatkumar Hegde

Donald Terry French

Chen-fang Chang

Oguz H Dagci

Giuseppe Conte

Chunhao J Lee

Yiran Hu

Chaitanya Sankavaram

Shiming Duan

Ognyan N Yanakiev

Kevin Andrew Gady

Azeem Sarwar

Patrick G Szymkowicz

Anupam Gangopadhyay

Steven E Muldoon

Hossein Javaherian

Yusheng Zou

Xiangxing Lu

Jun-Mo Kang

Chi-Kuan Kao

Shifang Li

Yongjie Zhu

Shailendra Kaushik

Insu Chang

Sai S V Rajagopalan

Zhiping Steven Liu

Vincenzo Alfieri

Ping Ge

Alberto Lorenzo Vassallo

Claudio Ciaravino

Francesco Castorina

David Sun

Carmen Pedicini

Xiaobin (Sharon) Li

Hongxun Gao

A computer-implemented method for predicting a quality of a battery includes receiving a first battery measurement data for a first duration of soaking the battery, the first duration shorter than or equal to the soaking. The method further includes computing a plurality of features based on the first battery measurement data. The method further includes predicting, based on the plurality of features, a state of the battery after completion of the soaking. The method further includes outputting suitability of the quality of the battery based on the state of the battery as predicted.

Predictive battery health detection from pack soaking behavior

A vehicle operates a system and method of predicting a thermal runaway event in a battery pack of the vehicle. The battery pack includes a battery cell. The sensor obtains measurements of a parameter of the battery cell at a plurality of times. The processor is configured to determine a value of at least one feature of the battery cell from the measurements of the parameter, determine a likelihood of the thermal runaway event from the value of the at least one feature, and take an action to prevent the thermal runaway event from occurring based on the likelihood.

Architecture and method of thermal runaway prognostics with multi-parameter data fusion

A distributed learning system of a vehicle includes: a control module configured to: control a plant of the vehicle using a policy; send signals to a learning module including information on an impact of the control on the plant; and selectively control the plant using exploratory control; and the learning module, where the learning module is separate from the control module and is configured to selectively update the policy based on (a) the signals from the control module, (b) state parameters resulting from the control of the plant using the policy, and (c) performance feedback determined based on the control of the plant using the policy and the selective control of the plant using exploratory control, where the control module is configured to receive the exploratory control from the learning module.

Distributed embedded controller implementation for self-learning controls

A system in a vehicle includes memory to store driving history and charging history of the vehicle. The system also includes a processor to obtain a predicted battery life of one or more battery packs of the vehicle based on the driving history and the charging history, obtain a targeted battery life of a user indicating a mileage goal for a specific charge capacity of the one or more battery packs, and determine a difference between the predicted battery life and the targeted battery life. The processor solves an optimization problem to determine a future charging strategy to achieve the targeted battery life and controls an onboard charging system or an external charger based on the future charging strategy or controls routing or navigation based on the future charging strategy.

Goal-based electric vehicle charging

A solar loading-based system includes a memory, a disturbance prediction module, a cabin temperature estimation module and a thermal control module. The memory stores a cabin thermal load model of an interior cabin of a host vehicle and a solar load prediction model. The disturbance prediction module: receives signals indicative of states of cabin thermal actuators and comfort metrics; and predicts an effect of solar loading over a known portion of a predicted route including predicting cabin temperatures based on the solar load prediction model, the states of the cabin thermal actuators, and the comfort metrics. The cabin temperature estimation module, based on the cabin thermal load model, determines a first comfort metric based on the predicted cabin temperatures. The thermal control module controls cabin thermal actuators to adjust cabin states, including the first comfort metric, to respective target values based on the predicted effect of solar loading.

Solar-load prediction for vehicular cabin thermal actuator control

A computer for an energy management system of an electric vehicle includes a processor. The computer further includes a memory including instructions such that the processor is programmed to determine a value function V based on a plurality of actions U in a plurality of states S. The processor is further programmed to select an action associated with a highest reward value at a current state S. The action U is an HVAC subsystem variable. The state S is a traction power drawn from a rechargeable energy storage system (RESS) to operate a traction subsystem, a base power input drawn from the RESS to operate an HVAC subsystem, a nominal reference cabin heat input set-point determined by the local HVAC processor, an acceleration of the electric vehicle, a current vehicle speed, an average vehicle speed, and a calibrated average vehicle speed estimate.

Energy management system for an electric vehicle

An energy management system for a vehicle is disclosed. The vehicle includes one or more power sources configured to provide power to one or more recipients. The system includes a controller configured to determine an arbitration vector based at least partially on a state vector and an initial transformation function. The arbitration vector is determined as one or more points for which the initial transformation function attains a maximum value. The controller is configured to determine a current reward based on the arbitration vector and the state vector, the current reward being configured to minimize energy loss in the power sources. The controller is configured to determine an updated transformation function based at least partially on the initial transformation function and a total reward. The controller is configured to arbitrate a power distribution based in part on the updated arbitration vector.

Intelligent energy management system for a vehicle and corresponding method

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 diagnostic system for a fuel injector includes a plurality of sensors to sense vehicle data. A controller includes a fuel injector diagnostic module configured to receive the vehicle data during operation of the vehicle and to selectively identify at least one of a fuel injector with a stuck armature and a fuel injector with pintle fatigue.

Early detection of fuel injectors with manufacturing issues

A method for controlling continuous and discrete actuators (e.g., modes) in a powertrain system includes receiving preview information from a sensor(s) describing an upcoming dynamic state at a future time point, and providing control inputs for the actuators to a controller that includes the preview information. The input set collectively describes a future torque or speed output state at the future time point. The controller processes the input set via a dynamical predictive model, in real time, to determine control solutions to take at the present time point for implementing the dynamic state at the future time point. A lowest opportunity cost control solution is determined and optimized. The controller executes the optimized solution at the present time step.

Model-predictive control of a powertrain system using preview information

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 controls multiple continuous actuators to achieve a discrete mode of operation in a system. The method may include determining a desired output state of the system, including processing a control input set for the multiple continuous actuators via a dynamical predictive model of the system, and then processing the control input set via the dynamical predictive model to determine possible control solutions for achieving the desired output state of the system at a calibrated future time point. The method may include using a cost function logic block to identify, from among the possible control solutions, a lowest-cost control solution for executing the discrete mode at the future time point, processing the lowest-cost control solution through a real-time optimization logic block to determine an optimized solution for the discrete mode, and then executing the optimized solution at the future time point.

Predictive torque management for powertrain having continuous actuators and multiple discrete modes

A two-stage air charging system for an internal combustion engine with mixed exhaust gas recirculation includes a high pressure exhaust gas recirculation loop, a low pressure exhaust gas recirculation loop, an air throttle system, a turbo air charging system, and an electric air charging system. A method to control the system includes monitoring desired operating target commands and operating parameters. Feedback control signals are determined based upon the monitored desired operating target commands and the monitored operating parameters. The two-stage air charging system is controlled based on system control commands for each of the high pressure exhaust gas recirculation loop, the low pressure exhaust gas recirculation loop, the air throttle system, the turbo air charging system and the electric air charging system.

Method and apparatus for controlling a two-stage air charging system with mixed EGR

An engine assembly includes an engine and a plurality of actuators. The plurality of actuators includes a first turbine serially connected to a second turbine, the first turbine being a relatively high pressure turbine and the second turbine being a relatively low pressure turbine. A controller is configured to transmit respective command signals to the plurality of actuators. The controller is programmed to obtain respective transfer rates for the plurality of actuators based at least partially on an inversion model. The controller is programmed to control an output of the engine by commanding the plurality of actuators to respective operating parameters via the respective command signals. Prior to obtaining the respective transfer rates, the controller is programmed to determine a respective plurality of desired values and respective correction factors for the plurality of actuators.

Air charging control of engine assembly with multiple turbines

Disclosed are engine torque and emission control (ETEC) systems, methods for using such systems, and motor vehicles with engines employing ETEC schemes. An ETEC system is disclosed for operating an internal combustion engine (ICE) assembly. The system includes an engine sensor for monitoring engine torque, an exhaust sensor for monitoring nitrogen oxide (NOx) output of the ICE assembly, and an engine control unit (ECU) communicatively connected to the engine sensor, exhaust sensor, and ICE assembly. The ECU is programmed to: receive desired engine torque and desired NOx output data; determine, from current engine torque and current NOx output data, desired engine torque, and desired NOx output, desired engine operation and exhaust operation references; determine, from the desired engine operation and exhaust operation references, an engine operation control command and an exhaust operation control command; and regulate operation of the ICE assembly via the engine operation and exhaust operation control commands.

Growing community of inventors

Macomb, MI, United States of America

Ibrahim Haskara