Qnovo and Sonatus team up to revolutionize EV battery safety with AI

A game-changing alliance promises near-flawless battery fault detection for EVs. Could this be the key to faster, safer electric cars?

, and Administrator

2026 March 19 . 10:02 PM

1 min read

The image shows a 3D model of a car battery with its various components labeled. It is a graphical... — The image shows a 3D model of a car battery with its various components labeled. It is a graphical representation of the electronic device, with labels indicating the various components and their functions.

Qnovo and Sonatus team up to revolutionize EV battery safety with AI

Qnovo and Sonatus have joined forces to improve battery safety in electric vehicles. The partnership combines Qnovo's Health & Safety Diagnostics with Sonatus AI Director, aiming to speed up deployment and cut engineering costs. Together, they claim the system can predict battery faults with 98.7% accuracy.

The two companies have integrated their technologies to create a production-ready solution for automakers. By merging Qnovo's diagnostics with Sonatus' AI platform, they reduce traditional integration time from months to just days. This efficiency allows car manufacturers to adopt advanced battery safety features more quickly.

Qnovo's diagnostics have undergone extensive testing, covering millions of kilometres and 200 million device deployments without any reported safety incidents. The addition of Sonatus AI Director further refines accuracy by analysing contextual data in real time.

Jeff Chou, CEO and co-founder of Sonatus, described the collaboration as a major shift in battery intelligence for software-defined vehicles (SDVs). Nadim Maluf, CEO of Qnovo, also highlighted the need for smarter battery management as electric vehicles become more reliant on software. Despite these advancements, no global automakers have publicly confirmed plans to use Qnovo's diagnostics in their vehicles as of March 2026.

The partnership delivers a ready-to-use battery safety solution with near-perfect fault prediction. Automakers can now implement the technology faster and at lower cost. However, adoption by major car brands remains unconfirmed, with no reports of active use in any region.