Florida International University

Asadullah Khalid

Arif I Sarwat

Aditya Sundararajan

Alexander Joseph Stevenson

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.

Systems and methods for detecting an attack on a battery management system

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.

Systems and method for testing battery management systems

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).

Growing community of inventors

Miami, FL, United States of America

Asadullah Khalid