AI Kinetic Energy Recovery
Machine learning for vehicle and industrial kinetic energy recovery
The Problem
“Maximize kinetic energy recovery across grid assets”
Organizations face these key challenges:
1
Recovery systems operate with conservative, static setpoints that miss transient opportunities and vary widely by asset and operating mode
2
Limited real-time observability and attribution makes it hard to quantify recovered kWh, diagnose losses, or prove ROI to stakeholders
3
Aggressive recovery can trigger power quality issues, inverter/thermal limits, or increased mechanical wear, creating a tradeoff operators struggle to manage
Impact When Solved
5–20% higher recovered energy through real-time, asset-specific optimization3–10% peak demand reduction via coordinated buffering and dispatch under tariff constraints20–40% fewer trips/constraint violations and 5–15% longer component life from predictive, constraint-aware control
The Shift
Real-World Use Cases
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