Accurate Battery SoH Estimation at Fleet Scale with EVE-Ai™

Traditional State of Health diagnostics are accurate—but slow, costly, and impossible to scale across real-world fleets.

In this case study, Electra demonstrates how EVE-Ai™ Battery Fleet Analytics achieves lab-level SoH accuracy (1.6% average error) using only operational field data from electric 2- and 3-wheeler fleets operating under harsh conditions in India.

Based on a real customer deployment and validated against diagnostic ground truth, the study shows how physics-informed AI enables scalable SoH monitoring, improved uptime, reduced OPEX, and over 2600% modeled ROI,without interrupting operations or relying on laboratory testing.