Can AI and ML help in mitigating thermal runaway risks in EV batteries?
The occurrence of thermal runaway in EV batteries presents serious risks, including fire hazards and safety concerns for drivers and passengers. As a result, companies are actively seeking solutions to prevent and mitigate these risks. This article examines how cutting-edge technologies like AI and machine learning (ML) can be utilized to enhance battery safety and reduce the likelihood of thermal runaway.
In our recent article, "Decoding thermal runaway in EV batteries," we examined the serious issue of thermal runaway in electric vehicle batteries. This phenomenon poses significant risks, including fire hazards and safety concerns for drivers and passengers, especially if it occurs while a vehicle is parked in a garage or basement, or during the storage and transport of EV batteries. The article also discussed various mitigation strategies to prevent thermal runaway, such as developing safer battery chemistries and implementing robust battery management systems (BMS) to monitor and control temperature, usage of passive and active safety features such as thermal barriers and thermal interface materials etc. to reduce the risk of thermal runaway incidents.
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