Tariff optimization shows where flexible charging can use price, peak, PV/battery, and market signals while protecting departures. Numeric savings depend on reviewed assumptions.
Tariff and peak scenarios
Readiness constraint first
Savings tied to assumptions
Teams comparing static tariffs, dynamic tariffs, peak exposure, PV/battery options, and site constraints.
Fleet teams that need lower cost but cannot trade away vehicle readiness.
Multi-depot or advanced-tariff planning where local flexibility and reporting become more valuable.
Compare price windows, peak exposure, grid limit, schedule flexibility, and local asset signals.
Show which assumptions change economics most: route timing, consumption, tariff spread, grid limit, PV/battery availability.
Separate capability descriptions, modelled scenarios, and measured results so savings are discussed with the right context.
View details| Evidence layer | How to use it | Support required |
|---|---|---|
| Capability | Fenexity schedules charging around tariffs and constraints | Feature status |
| Modelled | Scenario-dependent cost range with assumptions | Reviewed assumptions |
| Measured | Customer-specific before/after result | Validated data and publication permission |
| Excluded | Universal savings promise | Not used |
This page intentionally avoids universal savings percentages. Ranges depend on model assumptions, data source, and sensitivity factors.
