AI Technology
Battery Health Prediction
Predictive battery health monitoring for vehicle fleets and IoT-powered assets — using voltage pattern analysis to predict battery failure 5–14 days before it occurs, eliminating unexpected breakdowns.
How it works
Battery Prediction monitors voltage decay patterns and charging behaviour to identify batteries approaching end-of-life before they fail in the field.
Step 1
Continuous Voltage Monitoring
The IoT device monitors vehicle battery voltage at 1-minute intervals, building a high-resolution picture of charging and discharge patterns.
Step 2
Pattern Analysis
AI compares the current battery's voltage signature against a library of 50,000+ battery profiles — identifying anomalies like slow recharge, voltage drop under load, and overnight drain.
Step 3
Failure Prediction
When the model confidence for battery failure within 14 days exceeds 80%, a maintenance alert is issued to the fleet manager — with estimated days remaining.
Data inputs
- Real-time battery voltage (1-minute resolution)
- Engine start/stop voltage patterns
- Ambient temperature data
- Vehicle age and battery install date
Outputs
Battery Health Score (%)
Current battery health percentage per vehicle
Predicted Failure Alert
14-day advance warning for batteries approaching failure
Maintenance Queue Entry
Automatic work order creation in connected CMMS systems
Example insight
Vehicle ABUJA-TRK-017 battery health: 23%. Predicted failure within 8 days. Last replacement: 31 months ago. Recommend immediate replacement before next long-haul trip.
See Battery Health Prediction on your fleet
Talk to our security architects about vehicles, fleets, churches, schools, and enterprise assets.