Eatron Technologies have announced that they are utilising Artificial Intelligence (AI) as a potential method of predicting and preventing battery fires in electric vehicles (EVs).

Vehicle fires are nothing new but EV fires are far more dangerous as they are much more difficult to extinguish compared to a combustion engine fire. Therefore, the fires caused by EVs are far more deadly and prove a greater risk to drivers.

As a result, vehicle manufacturers and the wider automotive industry now face the challenge of regaining the trust of consumers whose opinions of EVs may have been negatively impacted by recent publicity.

Dr. Umut Genc, CEO at Eatron Technologies, said:

“The reality is that EV battery fires are incredibly rare, but even one is one too many. As an industry, we need to ensure the number of catastrophic battery failures reaches zero, and then stays there. Our intelligent, connected and safe automotive-grade battery management software has demonstrated that AI holds the key to achieving this,”

The causes of battery failure are complex, and often involve a combination of factors. One of the most common causes – lithium plating – occurs when metallic lithium deposits form around the anode. This is most likely during fast charging at low temperatures, and over time these deposits erode the performance of the battery.

Left unchecked, this can lead to the growth of dendrites, needle-like structures that can pierce through the separator between the anode and the cathode, causing a short circuit within the cell. This in turn leads to a rapid self-discharge that can initiate thermal runaway, a self-sustaining chain reaction that is difficult to extinguish.

Detecting lithium plating without opening the battery cell and examining the electrodes – which is largely impossible once mounted in a vehicle – is a challenge that has been the subject of intense research. And while various techniques have been developed over the years, each has their own limitations, particularly when it comes to distinguishing lithium plating from other degradation mechanisms.

However, by using Artificial Intelligence, Eatron Technologies has been trialling the potential to detect lithium plating and predict when it will occur.

Dr. Umut Genc, continued:

“Using a technique called feature extraction, we transform the raw health data coming from the battery into a format that makes anomalies easier to identify. By combining this with our proprietary AI pipeline that accurately captures battery behaviour, our AI diagnostics can predict cell failures before they occur.”

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