Patents by Inventor Asadullah Khalid

Asadullah Khalid has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11593479
    Abstract: Systems and methods for detecting and/or identifying an attack on a battery management system (BMS) or a battery system. The voltage and/or state of charge (SOC) of the BMS or battery system can be monitored, and one or more datasets can be obtained. A principal component analysis (PCA) based unsupervised k-means approach can be applied on the one or more datasets to monitor for irregularities that indicate an attack.
    Type: Grant
    Filed: May 25, 2022
    Date of Patent: February 28, 2023
    Assignee: THE FLORIDA INTERNATIONAL UNIVERSITY BOARD OF TRUSTEES
    Inventors: Arif I. Sarwat, Asadullah Khalid
  • Patent number: 11480626
    Abstract: Testbeds for battery management systems (BMSs) and/or batteries, as well as methods of using the same, are provided. A testbed can be a control-hardware-in-the-loop (CHIL) testbed and can include a simulation bench including a battery cell simulator, a temperature simulator, and/or a real-time simulator. The simulator bench can further include a programmable power supply, a relay, a resistor, and/or a communication protocol.
    Type: Grant
    Filed: March 28, 2022
    Date of Patent: October 25, 2022
    Assignee: THE FLORIDA INTERNATIONAL UNIVERSITY BOARD OF TRUSTEES
    Inventors: Arif I. Sarwat, Asadullah Khalid, Alexander Stevenson
  • Publication number: 20210123975
    Abstract: Systems and methods for forecasting of State of Charge (SOC) of lithium ion batteries are provided. A multi-step forecasting process with experimentally obtained decreasing C-Rate datasets together with machine learning can be used. The multi-step approach can combine a univariate technique with machine learning techniques. An Auto Regressive Integrated Moving Average (ARIMA) and/or Holt Winters Exponential Smoothing (HWES) can be combined with each other and/or with machine learning techniques such as Multilayer Perceptron (MLP) and Nonlinear autoregressive neural network with external input (NARX-net).
    Type: Application
    Filed: October 26, 2020
    Publication date: April 29, 2021
    Applicant: The Florida International University Board of Trustees
    Inventors: Arif Sarwat, Asadullah Khalid, Aditya Sundararajan
  • Patent number: 10969436
    Abstract: Systems and methods for forecasting of State of Charge (SOC) of lithium ion batteries are provided. A multi-step forecasting process with experimentally obtained decreasing C-Rate datasets together with machine learning can be used. The multi-step approach can combine a univariate technique with machine learning techniques. An Auto Regressive Integrated Moving Average (ARIMA) and/or Holt Winters Exponential Smoothing (HWES) can be combined with each other and/or with machine learning techniques such as Multilayer Perceptron (MLP) and Nonlinear autoregressive neural network with external input (NARX-net).
    Type: Grant
    Filed: October 26, 2020
    Date of Patent: April 6, 2021
    Assignee: The Florida International University Board of Trustees
    Inventors: Arif Sarwat, Asadullah Khalid, Aditya Sundararajan