Smart Battery Research

The proposed project seeks to develop a Machine Learning-based software solution that accurately measures the capacity, State of Health (SoH), State of Charge (SoC), and cycle count of non-smart batteries utilized in mobile fleets. The project’s primary objective is to bridge the gap between smart and non-smart batteries by monitoring non-smart battery capacity and other pertinent parameters. It includes conducting experiments on Lithium-Ion batteries to obtain valuable data and parameters, which will be utilized to develop mathematical models and Machine Learning algorithms for predicting those parameters for non-smart batteries. The aim is to integrate non-smart batteries into SOTI’s XSight dashboard, providing customers with precise information for a broader range of battery models. This project is expected to benefit SOTI and its customers significantly, while also contributing to sustainable technology development.

Faculty Supervisor:

Arvind Gupta;Huaxiong Huang

Student:

Partner:

SOTI Inc

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Toronto

Program: