EV Charging Optimization
Cut your charging plaza energy costs by up to 30%
Compare smart charging strategies side by side. See exactly how much each approach saves — then pick the one that fits your operation.
Now with AI-powered optimization that learns from your dataThree approaches to cutting your energy bill
From simple scheduling rules to AI — see which strategy saves the most for your setup.
Rule-Based
Deterministic heuristics like FIFO (First In, First Out), Fair Share, and Valley Filling. Fast, predictable, easy to understand — but don’t optimize for cost or constraints.
MPC (Model Predictive Control)
Mathematical optimization over a rolling time horizon. Uses demand and price forecasts to minimize cost — but only as good as its predictions.
RL (Reinforcement Learning)
AI that learns your cheapest charging schedule from real data. No forecasts needed — it adapts automatically as conditions change.
Learn moreSee the savings before you commit
Model your exact plaza setup, run the numbers, and compare approaches — all before changing a thing.
Configure scenarios
Set charger counts, power limits, EV arrival patterns, pricing profiles, solar, and battery storage.
Compare side by side
Run multiple algorithm families on the same scenario and see the differences instantly.
Visualize results
Interactive charts: power flow, SOC curves, cost accumulation, and energy distribution.
Learn & understand
Built as a teaching tool — understand why algorithms behave differently, not just that they do.
Cumulative cost over 24 hours
Same plaza, same day — see how each algorithm family accumulates cost differently
Rule-Based
€203
Baseline
MPC
€151
26% cheaper than Rule-Based
RL
€138
32% cheaper than Rule-Based
Want even bigger savings?
Our AI-powered optimizer learns the cheapest charging schedule from your real data — no forecasts or manual tuning required.
Based on peer-reviewed reinforcement learning research from TU Delft and ETH Zurich
Compare charging strategies in seconds
Three steps. No setup headaches. Just pick, run, and see.
1
Pick a scenario
Choose a preset or build your own: set charger count, EV schedules, pricing, solar, and battery.
2
Run all three
One click simulates Rule-Based, MPC, and RL on the exact same scenario.
3
See the difference
Interactive charts reveal where each approach wins and why — cost, peak demand, constraint compliance.
Algorithm comparison
How the three families stack up across key dimensions.
| Dimension | Rule-Based | MPC | RL |
|---|---|---|---|
| Needs forecasts? | No | Yes | No |
| Handles uncertainty? | No | Partially | Yes |
| Optimality | Low–Medium | High (with good data) | High |
| Constraint handling | Manual | Built-in | Learned |
| Computation | Instant | Medium | Slow to train, fast at inference |
| Best for | Quick baselines | Known environments | Uncertain, real-world conditions |
| Want to understand how RL works? Explore the AI deep dive | |||
Run your first simulation in under a minute
See how much you can save
Model your plaza, run three strategies, and compare costs instantly.
Explore AI optimization
See how reinforcement learning can find savings that traditional approaches miss.