AI-Powered Thermal Runaway Detection: A Breakthrough in EV Battery Safety

A team of engineers led by a University of Arizona doctoral student has introduced a novel method for preventing EV batteries from overheating. The method uses AI algorithms to predict areas of concern before they become dangerous. Many see this study as a breakthrough in the industry, especially considering the growing demand for EVs. Here's everything you need to know about AI's future role in preventing thermal runaway.

A team of engineers led by a University of Arizona doctoral student has introduced a novel method for preventing electric vehicle (EV) batteries from overheating, a problem that can lead to catastrophic failure.

The method uses artificial intelligence (AI) algorithms to predict areas of concern before they become dangerous, an advance that could pave the way for safer and more efficient EVs.

Here's everything you need to know about AI's future role in preventing thermal runaway.

Lithium-ion Batteries (LIBs)

To understand the significance of this research, it's important to know that lithium-ion batteries (LIBs) are the most common type of battery used in today's EVs.

These batteries work by using charged lithium ions to transfer energy across the unit, producing current for your electrical needs.

What makes LIBs so popular is that they can be charged by temporarily flipping polarity and sending the ions back to the negative pole of the unit.

Today's EVs rely on these devices for many reasons, including their decent life spans, relative lightness compared to alternatives, and exceptional energy density.

Notably, it's common for these batteries to use cells that are grouped to create the full EV pack, and most EV battery packs have thousands of cells.

What is Thermal Runaway?

The current multi-cell structuring of LIBs helps the batteries charge faster and achieve longer lifespans. However, it can create hotspots within the battery pack that can result in catastrophic failure.

When a single cell starts to malfunction, it can heat up quickly, causing the surrounding cells to experience increased temperature and potentially leading to more failure.

This domino effect is called thermal runaway, and it's one of the main problems faced by EVs today.

Thermal Runaway (TR) can reduce performance, cause battery decomposition, and even explosions, making it a real concern for EV owners.

Several factors can cause Thermal Runaway, including battery failures such as melting of the separator, decomposition of the cathode, or an adverse Li-electrolyte reaction.

These short circuits can occur quickly and result in nearby bystanders getting injured due to fire and explosions.

There are plenty of stories of people waking up to house fires or other terrifying moments due to their EV battery igniting, so solving this problem has become a primary concern for researchers globally.

Rising Temperature

The need to reduce TR has become more important over the last few years due to multiple factors.

The rise in both EV usage and global temperatures has made a dangerous scenario with more lives at risk than ever.

These factors make keeping batteries cool essential to achieving a greener future.

AI Thermal Runaway Study

A study published in the Journal of Power Sources demonstrates how an advanced AI algorithm coupled with sensors could be the key to eliminating thermal runaway once and for all.

The study, led by Basab Goswami, uses driver data simulations to mimic EV battery usage under daily driving conditions.

Multiphysics and machine learning models that leveraged thermal, electrochemical, and degradation sub-models were used to determine key moments when TR became noticeable.

From there, the AI systems reinforced the data, allowing them to predict and identify overheating cells faster than any optical solution.

AI Thermal Runaway Test

Researchers sought to gain a better understanding of how a solid electrolyte interface degrades on a negative electrode under various conditions.

The team used real driver data and battery states such as the constant charge/discharge and driving cycles to test the battery's heat signature.

To accomplish this task, the team created a battery that had special thermal sensors wrapped around it.

The temperature sensors provided detailed spatial and temporal temperature data that was then combined with historical data and fed to the AI algorithm.

This data included key situations, environments, driver activities, and technical issues.

Goswami's Algorithm

The Goswami Algorithm is unique in many ways. For one, it’s the first AI machine-learning model used to predict TR.

This multiphysics model was only made possible thanks to new AI systems such as vector modeling.

These advanced systems can analyze massive amounts of data and point out correlations or complex patterns far beyond human capabilities.

Consequently, the modeling method enabled the team to create realistic data on EV-driving behavior.

AI Thermal Runaway Test Results

The study's results are impressive. For one, the team was successful in its goal to accurately and precisely predict TR in LIBs consistently.

The AI was very precise and could even determine where the thermal runaway began, alerting of the danger and preventing further damage.

Now, the team seeks to expand its research, which could one day help create safer EVs for all.

AI Thermal Runaway Benefits

There are many benefits that this research brings to the market.

For one, the AI algorithm is far less expensive than using other methods to prevent Thermal Runaway.

In the past engineers, including the ones in this study, have

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