Understand charging windows better. And avoid peaks more deliberately.
Is your fleet charging exactly when site load and electricity prices are already high? Analyze charging, load, and timing data to see faster which windows, routines, and peak patterns are creating avoidable cost.
- Logistics companies with e-fleets
- Industry with e-vehicles
- Fleet managers
- Energy managers
The Problem
When charging times are set operationally instead of with data, site peaks, expensive windows, and avoidable pressure on connection capacity become normal.
- Charging sessions collide with already high site load
- Favorable and unfavorable windows are not compared cleanly
- Price and peak patterns stay hard to see day to day
- Charging strategies are difficult to justify internally
The Solution
dAIve Predict turns charging and load data into a better decision base: expose peaks, compare time windows, and derive a more grounded charging strategy.
See when charging power collides with site load
Identify windows where parallel charging creates peaks and avoidable costs.
Compare charging windows by cost and utilization
Review which routines perform better under similar operating conditions.
Derive a better charging strategy from real data
Use patterns and anomalies to plan charging times more deliberately and explain changes internally.
potential electricity cost savings when charging windows and peak patterns are compared with data instead of being managed purely by habit.
Indicative value from experience, to be verified with your data in a pilot.
How it works
Export charging data
Charging times, load profiles, electricity prices.
Perform analysis
dAIve Predict detects load patterns, peaks, and unusual time windows.
Compare windows
See which charging periods behave better under similar conditions.
Derive strategy
Use the results for more grounded charging decisions.
Every unchecked charging peak pushes cost and load upward.
Start your 14-day trial and use your first dataset to see which charging windows deserve review first.