The University of Arizona on Thursday proposed a way to predict and prevent temperature spikes in the lithium-ion batteries commonly used to power electric vehicles.
According to a statement, the finding was part of a new research by its doctoral student that prevents car battery fires using multiphysics and machine learning models.
The paper “Advancing Battery Safety,” led by College of Engineering doctoral student Basab Goswami, is published in the Journal of Power Sources.
Goswami and his advisor, aerospace and mechanical engineering professor Vitaliy Yurkiv, developed a framework that senses, predicts and identifies lithium-ion battery overheating, known as thermal runaway.
In the future, this framework could be integrated into an EV’s battery management system to stop a battery from overheating, thereby protecting drivers and passengers,” said Goswami in the statement.
“We need to move to green energy,” Goswami said, “but there are safety concerns associated with lithium-ion batteries.”
Working principle
Thermal runaway can be extremely dangerous and difficult to predict.
“The temperature in a battery will escalate in an exponential manner, and it will cause fire,” Goswami said.
An EV battery pack consists of closely connected battery “cells.” Today’s EVs can have more than 1,000 cells in each battery pack.
If thermal runaway occurs in one cell, nearby cells are highly likely to heat, too, creating a domino effect. If that happens, the entire battery pack of the electric vehicle could explode, Goswami said.
To prevent this, the researchers propose using thermal sensors – wrapped around battery cells – that feed historical temperature data into a machine-learning algorithm to predict future temperatures. The algorithm predicts when and where a runaway event is likely to start.
“If we know the location of the hotspot (the beginning of thermal runaway), we can have some solutions to stop the battery before it reaches that critical stage,” Goswami said.
Yurkiv said he was impressed by the accuracy of Goswami’s algorithm. Before his research, machine learning models had not been used to predict thermal runaway.
“We didn”t expect that machine learning would be so superior to predict thermocouple temperature and location of hotspots so precisely,” said Yurkiv. “No human would ever be able to do that.”
The research builds on a paper Goswami and Yurkiv published in January investigating the use of thermal imaging to predict runaway, which would require heavy imaging equipment constantly taking photos for review.
According to the statement, the solution Goswami and Yurkiv identified in their latest paper is lighter and more cost-effective.
