Q-BATT / BATTERY INTELLIGENCE RESEARCH
A deeper view of battery health.
Q-BATT explores how quantum sensing and causal AI could reveal more useful information about electric-vehicle batteries and support more informed fleet decisions.
Discuss the research
THE RESEARCH QUESTION
What could magnetic signals tell us?
Conventional operating measurements do not reveal every aspect of a battery’s internal condition. Q-BATT investigates whether additional sensing signals, interpreted alongside operational data, can help build a richer picture.
THE TECHNICAL APPROACH
Sense. Connect. Interpret.
01 / QUANTUM SENSING
Explore magnetic signatures.
The project considers diamond nitrogen-vacancy sensing as a way to investigate small magnetic variations associated with battery behaviour.
02 / DATA FUSION
Add operational context.
Sensing observations are considered alongside conventional battery measurements and operating conditions rather than in isolation.
03 / CAUSAL AI
Make the relationship clearer.
Causal analysis is explored to distinguish useful relationships from noise and support interpretable battery-health reasoning.
VALIDATION BEFORE CLAIMS
A research pathway, not a finished promise.
Sensitivity, robustness, data quality and practical integration all need careful evaluation. The aim is to understand where this approach could add value—not to assume that laboratory insight automatically translates into fleet performance.
FOR BATTERY TEAMS
Richer condition insight.
Explore whether complementary signals can help investigate degradation, uncertainty and battery-state assessment.
FOR FLEET OPERATIONS
More informed planning.
Investigate how interpretable health information could support maintenance conversations and operational decisions.
LET’S FIND THE NEXT USEFUL STEP
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