Gm Global Technolgoy Operations, Inc.

Cummins Inc.

Ohio State Innovation Foundation

Yue-Yun Wang

Ibrahim Haskara

Lei Hao

Frederic Anton Matekunas

Chol-Bum M Kweon

Suresh Gopalakrishnan

Paul Anthony Battiston

Garrett M Seeman

Ognyan N Yanakiev

Steven E Muldoon

Chandra S Namuduri

Yongjie Zhu

Donald Terry French

Chunhao J Lee

Dongxu Li

Chen-Fang Chang

Stephen Paul Levijoki

Charles Solbrig

Yongsheng He

Jeffrey S Piasecki

Vincenzo Alfieri

Rashmi Prasad

Shifang Li

Man-Feng Chang

Chaitanya Sankavaram

Jyh-shin Chen

Kevin Andrew Gady

Giuseppe Mazzara Bologna

Ruixing Long

Giuseppe Conte

Alberto Lorenzo Vassallo

Madhusudan Raghavan

Jun-Mo Kang

Thomas W Nehl

Venkata Prasad Atluri

Julian R Verdejo

Azeem Sarwar

Chen-fang Chang

Andrew C Baughman

Sai S V Rajagopalan

Junfeng Zhao

Scott T Feldmann

Ping Ge

Oguz H Dagci

Justin Bunnell

Jose C Zavala Jurado

Xiangxing Lu

Bojian Cao

Michael P Barker

Carlos Ildefonso Hoyos Velasco

Sriram Satya Srinivas Popuri

Gionata Cimini

Norman K Bucknor

Donald K Grimm

Christopher E Whitney

Brian James Koch

Alireza Fatemi

Chi-Kuan Kao

Gongshin Qi

Kumaraswamy V Hebbale

Muhammad Hussain Alvi

David Brian Brown

Samuel C Geckler

Yiran Hu

Michael J Cunningham

Raffaello Ardanese

Wen-Chiao Lin

Michael J Ruth

Zhen J Zhang

Shiming Duan

Joan M Wills

Scott E Parrish

Kushal Narayanaswamy

Anupam Gangopadhyay

Yusheng Zou

Charles Weldon Wampler, Ii

Zhiping Steven Liu

Anthony P Tata

Alfred Zhang

Claudio Ciaravino

Houchun Xia

Andres V Mituta

Jeremie Dernotte

Fang Wang

Francesco Castorina

David J Brooks

Rasoul Salehi

Taylor R Garrick

Harinath Atmaram

Yangbing Zeng

Cheryl Jeanette Stark

Marcello Canova

Brett B Thompson

David Sun

Carmen Pedicini

Jian Gao

Joerg Bernards

Scott R Gordon

Alessia Tuninetti

Xiaobin (Sharon) Li

Techniques are provided for battery imbalance diagnosis and mitigation. In one embodiment, the techniques involve isolating, via a first isolation unit, a first battery cell from a plurality of battery cells, determining, via a complex impedance measurement unit, a state of health of the first battery cell, and controlling, via a controller, the first battery cell based on the state of health of the first battery cell.

Battery imbalance diagnosis and mitigation

An example method includes receiving, at a module management unit (MMU) associated with a battery cell, a battery cell parameter measurement associated with the battery cell. The MMU in is communication with a battery radio frequency module via a first link. The method further includes performing, by the MMU, a first function based at least in part on the battery cell parameter measurement to generate a first signal, and transmitting, from the MMU, the first signal to the battery radio frequency module via the first link. The method further includes performing, by the battery radio frequency module, a second function based at least in part on the first signal to generate a second signal. The method further includes transmitting, from the battery radio frequency module, the second signal to a controller, and performing, by the controller, a third function based at least in part on the second signal.

Distributed battery management for a vehicle

A mixed chemistry battery including a sensing cell having a first chemistry, a battery cell having a second chemistry that is different than the first chemistry, where the battery cell is connected to the sensing cell in series. The mixed chemistry battery also includes a battery monitoring system configured to monitor a current flow through the sensing cell and the battery cell and to calculate a state-of-charge (SOC) of the sensing cell. The battery monitoring system is further configured to calculate a SOC of the battery cell based at least in part on the SOC of the sensing cell.

State of charge sensing for a mixed chemistry battery

A charging system includes: a charge port that receives shore power; a DC-to-DC converter or a motor inverter circuit; switches that connect the charge port and the DC-to-DC converter or the motor inverter circuit to battery packs; and a control module. The control module: determines open circuit voltages or states of charges of the battery packs; based on the open circuit voltages or the states of charges of the battery packs, determines whether to connect at least one of the battery packs to the charge port, the DC-to-DC converter, and the motor inverter circuit; and based on the determination of whether to connect at least one of the battery packs, control states of the switches to charge the at least one of the battery packs by selectively connecting the at least one of the battery packs to (a) the charge port, or (b) the DC-to-DC converter or motor inverter circuit.

Balancing of battery packs via DC-to-DC converters and motor inverter circuits

Some embodiments disclosed herein are directed to constraint handling for parameters of electric motors. In particular, embodiments of the present disclosure relate to handling constraints for parameters such as current, voltage, and torque associated with an electric motor. Other embodiments may be disclosed or claimed.

Constraint handling for electric motor parameters

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 vehicle, system and method for monitoring an occurrence of thermal runaway in a battery pack of the vehicle. The system includes a plurality of voltage sensors and a processor. The plurality of voltage sensors obtains a plurality of voltage measurements at each of a plurality of battery cells of the battery pack. The processor is configured to determine a mean value based on the plurality of voltage measurements, compare a voltage measurement obtained from a selected battery cell to the mean value, and generate a notification signal when a difference between the voltage measurement from the selected battery cell and the mean value is greater than or equal to a prognostic threshold.

Thermal runaway prognosis by detecting abnormal cell voltage and SOC degeneration

Systems and methods for diagnosing health of a battery using in-vehicle impedance analysis. The method includes receiving a sensed current measurement for a cell of the battery; generating a current profile as a function of the sensed current measurement, including pulses to a peak current, the pulses having a pulse frequency (w); applying a current with the current profile to the cell; for each pulse in the current profile, receiving a voltage measurement for the cell that is responsive to the pulse, calculating an impedance for the cell responsive thereto, the impedance comprising a real component at the pulse frequency (RZw), and an imaginary component at the pulse frequency (IZw), identifying a battery health problem when either RZw exceeds a preprogrammed first threshold for the pulse frequency, or when IZw exceeds a preprogrammed second threshold for the pulse frequency, and storing the RZw and the IZw for the cell.

Systems and methods for diagnosing health of a battery using in-vehicle impedance analysis

A trip energy estimation system for a vehicle includes a traffic speed module configured to determine an average traffic speed along a projected route, a path information module configured to output path information indicating route features along the projected route, a perceived speed module configured to output a perceived vehicle speed along the projected route based on the average traffic speed and the path information, and a dynamic driving module configured to calculate and output a predicted driver speed based on the perceived vehicle speed and a feedback input indicative of the predicted driver speed. The dynamic driving module is configured to execute a machine learning algorithm to calculate the predicted driver speed.

Traffic-info-incorporated NNT driver model for EV trip energy prediction

A system in a vehicle includes a controller to implement a neural network to provide current commands based on inputs. The inputs include a torque input. The system also includes a current controller to provide a three-phase voltage through an inverter based on the current commands from the controller. An electric traction motor provides drive power to a transmission of the vehicle based on injection of the three-phase voltage. The current commands resulting from implementation of the neural network are corrected based on estimated torque resulting from the injection of the three-phase voltage to the electric traction motor.

Neural network and torque feedback-based control of vehicle electric traction motor

A system for monitoring a battery module having a plurality of cells includes a sensor adapted to obtain respective cell voltages of the plurality of cells. The controller is adapted to receive data from the sensor, the controller having a processor and tangible, non-transitory memory on which instructions are recorded. The controller is configured to sort the respective cell voltages to create a voltage ranking. The voltage ranking is repeated with at least one new set of cell voltage readings obtained at a predefined checking frequency. Differences in the voltage ranking of the plurality of cells are analyzed. The controller is adapted to control operation of the battery module based in part on differences in the voltage ranking. The controller may be adapted to predict likelihood of a thermal runway event based in part on the differences in the voltage ranking.

Monitoring of battery module based on cell voltage rankings

A supervisory computer is used with an energy management system of an electric vehicle. The energy management system includes a battery system having a plurality of battery subsystems. One of the battery subsystems is an abnormal battery subsystem while the remaining battery subsystems are normal battery subsystems. The supervisory computer includes at least one processor and at least one non-transitory computer-readable medium. The processor monitors the remaining state of charge, capacity, and resistance of the battery system and monitor the remaining state of charge and capacity of the abnormal battery subsystem, calculate a low integration bound value, calculate a remaining energy value for all of the normal battery subsystems with respect to the low integration value, calculate a remaining energy value for the abnormal battery subsystem, and summate the remaining energy values of the normal and abnormal battery subsystems to determine a global remaining energy value for the battery system.

Energy management system for an electric vehicle

A system for torque control of an electric motor in a motor vehicle includes a power inverter that delivers a current to the electric motor to regulate the torque of the electric motor and a model predictive control module that sends a three-phase voltage to the power inverter to control the operation of the power inverter.

Linear time varying model predictive torque control

A power control system comprises a battery system including N battery packs. A plurality of contactors selectively connects N battery packs of a battery system in a first configuration supplying a first voltage level, a second configuration supplying a second voltage level, and a disconnected configuration. M power inverters connect the battery system to M electric machines, respectively. A controller is configured to determine when to transition between the first configuration and the second configuration. The controller is configured to transition between the first configuration and the second configuration by causing one of a positive torque transient and a negative torque transient to be generated by at least one of the M electric machines, transitioning the battery system from one of the first configuration and the second configuration to the disconnected configuration, and transitioning from the disconnected configuration to the other one of the first configuration and the second configuration.

Power control system for switching battery configuration during driving

A system for use with a direct current fast-charging (DCFC) station includes a controller and battery system. The battery system includes first and second battery packs, and first, second, and third switches. The switches have ON/OFF conductive states commanded by the controller to connect the battery packs in a parallel-connected (P-connected) or series-connected (S-connected) configuration. An electric powertrain with one or more electric machines is powered via the battery system. First and second charge ports of the system are connectable to the station via a corresponding charging cable. The first charge port receives a low or high charging voltage from the station. The second charge port receives a low charging voltage. When the station can supply the high charging voltage to the first charge port, the controller establishes the S-connected configuration via the switches, and thereafter charges the battery system solely via the first charge port.

Adaptive fast-charging of multi-pack battery system in a mobile platform having dual charge ports

Growing community of inventors

Troy, MI, United States of America

Yue-Yun Wang