Gm Global Technolgoy Operations, Inc.

Chaitanya Sankavaram

Azeem Sarwar

Shengbing Jiang

Shiming Duan

Yilu Zhang

Xinyu Du

Yue-Yun Wang

Layne K Wiggins

Garrett M Seeman

Xiangxing Lu

Paul Edward Krajewski

Ibrahim Haskara

Wen-Chiao Lin

Rafat F Hattar

Steven W Holland

Xiaoyu Huang

Dean G Sorrell

Ciro A Spigno

Calvin Wang

Xiaomeng Peng

Brian L Buck

Chunhao J Lee

Mark William Verbrugge

Xiaosong Huang

Chen-Fang Chang

Youssef A Ghoneim

Charles W Wampler, Ii

Bradley Allen Newcomb

Yao Hu

Stephen Paul Levijoki

Shifang Li

Louis G Hector, Jr

Sean R Wagner

Syed B Mehdi

Scott R Jeffrey

Ian J Macewen

Dnyanesh Rajpathak

Michelle H Wiebenga

Anil Bika

Steven Ward Majors

Ryan Gergely

Matthew Timothy Hamilton

Nicholas P Irish

David A Frank

Jonathan Pung

Wei Li

Todar Kuzmanov

John J Flood, Iv

Zifeng Peng

Michael B Gastmeier

Paul A Krajewski

Janelle Kane Rolando

Zining Zhang

Gregory G Tuayev-Deane

Ning Wang

A vehicle includes a system that performs method operating a battery pack of the vehicle. The system includes one or more sensors and a processor. The one or more sensors obtain a measured value of a battery parameter of the battery pack. The processor is configured to calculate an expected value of the battery parameter from a model of the battery pack, compare the measured value to the expected value to determine a blown fuse condition of the battery pack, determine an available current from the battery pack based on the blown fuse condition, and control an operational state of the vehicle based on the available current.

Diagnostics and mitigation of battery cell failure with passive disconnect

A system for monitoring a battery of a vehicle includes a current measuring circuit to measure a current of the battery comprising a plurality of cell groups connected to each other. A voltage measuring circuit is to measure voltages of the cell groups. A controller is configured to define a plurality operating regions in a current profile of the battery during a drive cycle of the vehicle. The controller is configured to filter the current and the voltages measured in the operating regions and calculate internal resistances of the cell groups in the operating regions based on the filtered current and voltages. The controller is configured to generate statistical values based on the internal resistances of the cell groups and determine whether one or more of the cell groups is faulty based on differences between maximum and minimum values of one of the statistical values across the cell groups.

Resistance estimation of high voltage battery packs during vehicle driving operation

A system for monitoring a battery of a vehicle includes a processor and a memory storing instructions which when executed by the processor configure the processor to receive first features including statistics of internal resistances of a plurality of cell groups in a battery pack of the battery, compute second features for the battery pack based on the first features, determine whether the battery pack is faulty based on one or more of the second features, and determine, in response to the battery pack being faulty, whether one or more of the cell groups is faulty based on one or more of the first features.

Health monitoring methods for early fault detection in high voltage battery packs used in electric vehicles

A battery electric system includes a battery, sensor suite, and controller. The sensor suite includes a current sensor, voltage sensor, and temperature sensor. The controller is operable for determining an estimated open circuit voltage of the battery at different time points using a current signal and a voltage signal from the current and voltage sensors, and an equivalent circuit model (ECM). The controller determines an ECM-based state of charge (SOC) of the battery at the different time points, via a calibrated SOC map, using the open circuit voltage and temperature, and calculates a sensor gain value using the ECM-based SOC and a coulomb counting-based SOC, both at the different time points. A control action is executed with respect to the battery when the sensor gain value exceeds a predetermined gain fault threshold, including generating a fault notification signal indicative of a fault of the current sensor.

Current sensor fault diagnostics

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 system includes a first most probable cause (MPC) module, a second MPC module, and an integrated MPC module. The first MPC module is configured to determine a first most probable cause of an issue on a vehicle based on at least one service procedure for the vehicle. The second MPC module is configured to determine a second most probable cause of the issue based on repair data for other vehicles. The integrated MPC module is configured to determine an integrated most probable cause of the issue based on the first and second most probable causes.

System and method for determining most probable cause of vehicle fault using multiple diagnostic techniques

A current measuring circuit measures a charging current of a battery comprising a plurality of cell groups while the battery is being charged using one of a plurality of charging systems. A voltage measuring circuit measures voltages of the cell groups. A controller defines a plurality operating regions in a charging current profile of the battery during a charging cycle, filters the charging current and the voltages measured in the operating regions, calculates internal resistances of the cell groups in the operating regions based on the filtered current and voltages, generates at least one of statistical values and distance metrics based on the internal resistances of the cell groups, and determines whether one or more of the cell groups is faulty based on the at least one of the statistical values and the distance metrics irrespective of the plurality of charging systems used to charge the battery.

Resistance estimation of high voltage battery packs during vehicle charging operation

Presented are control systems for operating rechargeable electrochemical devices, methods for making/using such systems, and vehicles with intelligent battery charging and charging behavior feedback capabilities. A method of operating a rechargeable battery includes an electronic controller receiving battery data from a battery sensing device indicative of a battery state of charge (SOC). Using this battery data, the controller determines a number of low SOC excursions at which the battery SOC is below a predefined low SOC threshold and a number of high SOC excursions at which the battery SOC exceeds a predefined high SOC threshold. The controller then determines if the number of low SOC excursions exceeds a predefined maximum allowable low excursions and/or the number of high SOC excursions exceeds a predefined maximum allowable high excursions. If so, the controller responsively commands a resident subsystem to execute a control operation that mitigates degradation of the rechargeable battery.

Intelligent motor vehicles and control logic for managing charging of traction battery packs

A method is disclosed for determining the state of health of an electric battery that includes a plurality of battery cells. The method includes the steps of measuring the cell voltage of each individual cell of the plurality of battery cells, and analyzing each measured battery cell voltage to determine the state of health of the corresponding battery cell.

Drooping cell detection and state of cell health monitoring

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 of performing fault diagnosis and analysis for one or more vehicles. The method includes: obtaining design failure mode and effect analysis (DFMEA) data that specifies a plurality of failure modes; receiving diagnostic association data; receiving vehicle operation signals association data; generating augmented DFMEA data that indicates a causal relationship between the diagnostic data and the first set of failure modes, and that indicates a causal relationship between the vehicle operation signals data and the second set of failure modes, wherein the augmented DFMEA data is generated based on the DFMEA data, the diagnostic association data, and the vehicle operation signals association data; and performing fault diagnosis and analysis for the one or more vehicles using the augmented DFMEA data.

Vehicle fault diagnosis and analysis based on augmented design failure mode and effect analysis (DFMEA) data

A control system for identifying and responding to effective system changes in a monitored system includes data collection, change identification, classifier construction, effective system change and parameter adjustment modules. The data collection module tracks parameters of the monitored system. The change identification module, based on the parameters, identifies: during training, performance changes and first system changes; and post training, a set of performance changes and second system changes. The classifier construction module, based on the performance changes and the first system changes construct a performance-system change classifier module. The performance-system change classifier module, based on the set of performance changes, determines possible system changes and respective probability values. The effective system change module, based on the second and possible system changes, determines effective system changes and respective probability values. The parameter adjustment module controls an actuator of the monitored system based on the effective system changes and the probability values.

Data-driven approach for effective system change identification

A method of root cause diagnosis of fault data from a vehicle includes identifying a first vehicle fault and selecting from field repair data a vehicle feature corresponding to the identified first vehicle fault. The method also includes identifying from the field repair data an effective repair of the identified first vehicle fault. The method additionally includes training and testing via a machine learning algorithm, a labor code classifier using the identified effective repair of the first vehicle fault and the selected vehicle feature corresponding to the identified first vehicle fault. The method also includes identifying and classifying, using the trained classifier, indistinguishable labor codes. Furthermore, the method includes communicating the identified and classified indistinguishable labor codes for diagnosing a root cause of real time first vehicle fault data. A computer-readable medium storing an executable computer algorithm for performing the root cause diagnosis of vehicle fault data is also envisioned.

Vehicle fault root cause diagnosis

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

Growing community of inventors

Sterling Heights, MI, United States of America

Chaitanya Sankavaram