Gm Global Technolgoy Operations, Inc.

Patricia M Laskowsky

Patrick Frost

Brian James Koch

Kurt M Johnson

Rory B Fraga

Mark William Verbrugge

Damon R Frisch

Aniket P Kothari

Jeffrey M Hutmacher

Todd P Lindemann

John E Novak

Dana J Suttman

Jin Shen

Charles W Wampler, Ii

Robert Charles Baraszu

James C Gibbs

Raghunathan K

Charles Weldon Wampler, Ii

Brandon R Jones

Bryan M Ludwig

Douglas S Cesiel

Ashley McAllister

Daniel R Baker

Iman Mohammadrezazadeh

Brett B Stawinski

Ramesh Rebba

Eugenie T Abboud

Ruggero Scorcioni

Jacob A St George

Alexander K Suttman

Bryan T Sauter

Sabrina Elaine Ridenour

Matthew A Thomas

Timothy Bresson

Ethan R Harrison

James Reilly

Christopher Rowe

Bridget A Beaton

Vincent DeSapio

Bo Xie

Jack M Williams

Giovanni Spoleti

Mark D Miller

A method includes obtaining sensor data for a vehicle having an energy storage system that is configured provide power for a home under certain conditions; obtaining home data as to a plurality of characteristics that pertain to power requirements for the home; and calculating, via a processor, a discharge time for the energy storage system based on the sensor data for the vehicle and the plurality of characteristics of the home.

Electric vehicle energy storage system time discharge estimation and management

A preemptive optimization of a power distribution event associated with providing an electrical power from a battery onboard a vehicle to a charging station offboard the vehicle. The preemptive optimization may include preemptively charging the battery in advance of the power distribution event and/or preemptively configuring the vehicle and the battery, such as with establishment of standby session, to provide electrical power from the battery to the charging station without providing the electrical power to the charging station until the charging station thereafter experiences an outage, interrupt, etc.

Preemptive optimization for offboard distribution of electrical power from a vehicle

A controller of a host system executes a method for detecting an external AC electrical device. While an AC charging inlet of the host system is electrically connected to the device via different vehicle-to-live (V2L) and jump-charge connections, the controller detects a control pilot voltage and a proximity voltage. When the control pilot voltage is 0V, the controller determines whether entry conditions are satisfied indicative of a desire to offload power from the host system to the device. When the entry conditions are satisfied, the proximity or control pilot voltage are modulated to generate a modulated voltage signal, which the controller compares to an expected voltage indicative of the device. Power is offloaded to the device when the modulated voltage signal matches the expected voltage.

Host system with AC charging inlet and method for identifying an electrical device connected thereto

Methods and systems are provided for monitoring a fuel injector of an internal combustion engine. In one embodiment, a method includes: receiving a set of feature data, the feature data sensed from a fuel injector during a fuel injection event; processing, by a processor, the set of feature data with a decision tree model to generate a prediction of a fault status; and selectively generating, by the processor, a notification signal based on the prediction.

Diagnostic methods and systems

Methods and systems are provided for monitoring a fuel injector of an internal combustion engine. In one embodiment, a method includes: receiving a set of feature data, the feature data sensed from a fuel injector during a fuel injection event; processing, by a processor, the set of feature data with a machine learning model to generate a prediction of a fault status; and selectively generating, by the processor, a notification signal based on the prediction.

Systems and methods are disclosed for estimating a state of a battery system such as a current-limited state of power and/or a voltage-limited state of power using a battery system model incorporating a nonlinear resistance element. Parameters of elements included in a battery cell model associated with a nonlinear resistance of a battery cell may be directly parameterized and used in connection with state estimation methods. By accounting for the nonlinear effect, embodiments of the disclosed systems and methods may increase available battery power utilized in connection with battery system control and/or management decisions over a larger window of operating conditions.

Battery state estimation systems and methods using a nonlinear resistance element

A number of variations include a method, which may include using at least a segment of voltage-based Battery State Estimation data, and using real-time linear regression, which may be a method of estimating future behavior of a system based on current and previous data points, to provide a robust and fast-adapting impedance response approximator. Linear regression may be performed by forming an RC circuit which is 'equivalent' to electrochemical impedance spectroscopy data and processing the runtime values of that RC circuit using any number of known real-time linear regression algorithms including, but not limited, to a weighted recursive least squares (WRLS), Kalman filter or other means.

State of charge estimator and methods of making and using the same

System and methods for estimating a relationship between a SOC and an OCV of a battery system included in a vehicle are presented. In certain embodiments, an initial relationship between an open circuit voltage ('OCV') and a state of charge ('SOC') of a cell of the battery system may be determined at a beginning of life of the cell. Changes in one or more stoichiometric points of a half-cell of the cell may be determined as the cell ages. Based on the determined stoichiometric point changes of the half-cell, an initial relationship between the OCV and the SOC of the cell may be adjusted to generate an updated relationship between the OCV and the SOC of the cell.

Systems and methods for estimating battery system parameters

A system for optimizing life of a battery pack in a plug-in vehicle includes sensors for measuring battery performance data, including an open-circuit voltage, charging current, and/or temperature of the battery pack, a GPS receiver, a user interface, and a controller. The controller executes a method to monitor degradation of the battery pack using the performance data, and determines driving and charging histories for an operator of the vehicle using the measured battery performance data and a position signal from the GPS receiver. The histories identify the days, hours, and locations at which the operator has driven the vehicle and charged the battery pack. The controller identifies a state of charge data bin missing performance data or containing old performance data. The controller then controls a charging operation of the battery pack via a charging control signal, and records the measured battery performance data for the identified SOC data bin.

Adaptive system and method for optimizing battery life in a plug-in vehicle

A method for estimating battery power capability in a torque-generating system having a battery pack with battery cells includes calculating a voltage spread as a difference between an average and minimum cell voltage of the battery pack. The method also includes increasing a calibrated voltage control limit by an offset that is based on a magnitude of the voltage spread, doing so when the minimum cell voltage is less than the control limit, and recording the offset in a memory location referenced by pack operating conditions. Further, the power capability is estimated using the recorded offset when the battery pack operates under conditions that are the same as the operating conditions, and then executing a control action of the system using the estimated power capability. A torque-generating system includes the battery pack, an electric machine, and a controller programmed to execute the above-described method.

Estimation of battery power capability in a torque-generating system

System and methods for estimating a future power capability of a battery system included in a vehicle are presented. In some embodiments, a method for estimating a future power capability of a battery system may include determining an initial battery voltage when the battery operates at a current limit and estimating a future battery current at a first time when the battery operates at the associated voltage limit based on the initial battery current. A future battery voltage at the first time when the battery operates at the associated current limit may be determined based on the initial battery voltage. An estimated voltage-limited power capability of the battery at the first time may be determined based on the future battery current and an estimated current-limited power capability of the battery at the first time may be determined based on the future battery voltage.

Systems and methods for estimating battery system power capability

System and methods for estimating a capacity of a battery system and/or a constituent cell and/or pack included in a vehicle are presented. In certain embodiments, a method for estimating a capacity of a battery system may include determining voltage-based state of charge ('SOC') data of a pack of the battery system based on measured pack voltage information and a relationship between an open circuit voltage ('OCV') and the SOC of the pack of at a beginning of life of the pack. Beginning of life SOC data of the pack may be determined based on coulomb counting measurements, and a ratio may be determined based on the beginning of life SOC data of the pack and the voltage-based SOC data of the pack. An estimated present capacity of the pack may be determined based on the beginning of life capacity of the pack and the ratio.

Battery system capacity estimation systems and methods

System and methods for estimating a capacity of a battery are presented. A measure of data skewness may be calculated from a battery system terminal voltage measurement. Inflection points in the data skewness of the terminal voltage measurement may align with transition points associated with a battery system. These transitions points may be associated with known SOCs determined from testing and/or characterization of the battery system. Using the detected transitions and associated SOCs and an indication of an accumulated charge provided to/from the battery between the transitions, an estimated capacity of the battery system may be determined.

Systems and methods for estimating battery pack capacity

A system for use with a battery and sensors operable for measuring battery data includes an interactive user interface and a controller programmed with battery degradation monitoring logic for estimating a state of the battery. As part of a method, the controller identifies data bins for which measured state of charge range-based battery performance data is missing or dated/stale, and automatically prompts an operator to execute an assigned task corresponding to the identified data bins. The controller records the battery performance data for the identified data bins upon completion of the assigned task, estimates the state of the battery using the recorded battery performance data and the battery degradation monitoring logic, and executes a control action with respect to the system using the estimated state. A virtual or actual reward feature may be displayed in response to completion of the assigned task.

System and method for monitoring battery degradation

Adaptive estimation techniques to create a battery state estimator to estimate power capabilities of the battery pack in a vehicle. The estimator adaptively updates circuit model parameters used to calculate the voltage states of the ECM of a battery pack. The adaptive estimation techniques may also be used to calculate a solid-state diffusion voltage effects within the battery pack. The adaptive estimator is used to increase robustness of the calculation to sensor noise, modeling error, and battery pack degradation.

Growing community of inventors

Ann Arbor, MI, United States of America

Patricia M Laskowsky