Texas Instruments Corporation

Gm Global Technolgoy Operations, Inc.

One Tech LLC

Yandong Zhang

Yevgen Barsukov

Dale Allen Blackwell

Jason Michael Battle

Konstantin Galburt

Brian James Koch

Damon R Frisch

Xidong Tang

Jian Lin

Andrew C Baughman

Brian Paul Alongi

Yasser Khan

Githin K Prasad

Kang Kang

A computer implemented method and system for predicting motor failure. The system and method employ artificial intelligence to train machine learning models to predict failure mechanisms within a machine. The system features at least one database configured to store machine performance indicator data. At least one server is configured to receive the indicator data and train machine learning models which are used by a processor to predict motor failure. The method for predicting motor failure is iterative, allowing the machine learning model to become more skilled at predicting machine performance outcomes in real-time.

System and method for predicting industrial equipment motor behavior

One embodiment of the invention includes a battery sense system. The system includes a temperature sensor configured to measure a temperature of a battery and a memory configured to store predetermined data associated with steady-state and transient behaviors of the battery relative to a depth of discharge (DOD) of the battery. The system also includes a controller configured to measure a voltage of the battery and to calculate a state of charge (SOC) of the battery based on the voltage, the predetermined data, and the temperature.

System and method for sensing battery capacity

Disclosed examples include systems and methods for determining an internal short condition in a battery. The method includes computing an estimated current value based on a measured battery voltage, a measured battery current, a measured battery temperature, and a model of the battery that includes steady state characteristics of the battery and transient characteristics of the battery. The method further includes computing an internal short current value according to the measured battery current and the estimated current value, and determining if a battery short condition exists according to the internal short current value.

System and apparatus for battery internal short current detection under arbitrary load conditions

Predictive battery pack cell balancing apparatus and methods are presented in which active bypass current switching is controlled according to initial balancing bypass current values to balance the cell depth of discharge (DOD) values by the end of a charging/discharging time period, and according to continuous balancing bypass current values representing an amount of bypass current needed to maintain a present relationship of the cell DOD values.

System and method for battery pack management using predictive balancing

A system includes a battery; an analog-to-digital converter coupled to the battery and capable of measuring an output voltage of the battery; a processor, receiving measured battery output voltages from the analog-to-digital converter; the processor using a first equivalent circuit model of the battery to estimate battery current when the battery operation is static; and the processor using a second equivalent circuit model of the battery to estimate battery current when the battery operation is dynamic.

Battery fuel gauge

A method of determining a state of health of a battery in real time includes estimating a parameter value associated with the state of health of the battery and determining one or more of a terminal voltage, an accumulated charge, a state of charge, and a temperature of the battery. The method further includes determining, via a computing device, a reserve capacity of the battery based at least in part on the estimated parameter value and one or more of the terminal voltage, the accumulated charge, the state of charge, and the temperature of the battery.

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Plano, TX, United States of America

Yandong Zhang