GRIDMIND
Dynamic Quantum Router for EV Fleets
Classical solvers decompose EV fleet routing, charging, and grid optimization into separate problems, losing 15-25% of optimization potential. DQR formulates them as a single unified QUBO and solves the coupled problem on D-Wave quantum annealers, delivering 25% less idle time and 40% lower grid peak variance.
EV Fleets Lose 30% of Operational Time to Charging Inefficiency
Three coupled NP-hard problems that classical solvers cannot solve together.
Three Coupled NP-Hard Problems
EV delivery vehicles spend ~30% of their time in non-productive states: waiting for chargers, detouring to stations, sitting idle from range constraints. This costs $18K-$25K per vehicle per year in lost productivity.
The root cause: routing, charging, and grid load are deeply coupled, but classical tools solve them independently. A vehicle's route determines when it needs charging; charger availability depends on other vehicles' routes; grid constraints limit simultaneous charging.
The Grid Time Bomb
A 200-vehicle fleet drawing 10-20 MW simultaneously at 5-7 PM causes $50K-$200K in annual demand charge penalties and requires $2-5M in infrastructure upgrades. Grid operators face $50B in upgrade costs if EV fleet charging stays uncoordinated.
Unified QUBO Formulation on D-Wave Quantum Annealers
Four integrated components solve the coupled routing-charging-grid problem as one.
QUBO Formulation Engine
Translates the coupled routing-charging-grid problem into QUBO matrices. Dynamically adjusts constraint weights based on real-time conditions. Adaptive penalty calibration maintains feasibility guarantees.
Quantum Optimization Core
D-Wave Advantage (5000+ qubits, Pegasus topology). Topology-aware minor embedding, chain strength optimization, multi-sample aggregation. Hybrid quantum-classical solver for large-scale problems.
Grid-Aware Dispatch
Integrates utility SCADA data and smart meter aggregation. Coordinates fleet charging with renewable generation profiles. Creates two-sided value: fleet savings + grid operator demand response revenue.
Edge Execution Layer
Edge computing at fleet depots and charging hubs. Real-time route updates pushed to vehicles. OCPP 2.0.1 charger integration. Continuous re-optimization on disruption events.
EV Fleet Adoption at 35% CAGR Creates an Optimization Vacuum
Quantum-Coupled Optimization vs. Classical Decomposition
vs. Classical Route Optimizers (Google OR-Tools)
Classical tools decompose the problem. DQR solves routing + charging + grid as one unified optimization, capturing coupling effects that sequential solvers miss entirely.
vs. Optibus / Geotab EV
Incumbent fleet software bolts EV constraints onto ICE-era tools. DQR is EV-native from the ground up, with charging as a first-class optimization variable, not an afterthought.
Two-Sided Revenue Model
Fleet operators pay for routing optimization. Grid operators pay for demand response coordination. Both benefit from the same quantum-optimized schedule, unique market position.
Scaling with Quantum Hardware
As D-Wave qubit counts grow, DQR handles larger fleets without algorithm redesign. Quantum advantage widens with problem size, the opposite of classical diminishing returns.
From Pilot Deployments to Full SaaS Platform
Pilot Deployment
Deploy with 3 mid-size fleet operators (50-200 vehicles each). Validate QUBO formulation against classical baselines. Measure idle time reduction and cost savings in production environments.
Grid Integration & SaaS Launch
First utility partnerships for demand response revenue sharing. SaaS platform v1 with self-service onboarding. Edge computing deployment at fleet depots. OCPP 2.0.1 charger integration.
Enterprise Scale
Scale to 500-2000 vehicle fleets. D-Wave Hybrid solver integration for large-scale problems. Real-time re-optimization on disruption events. Multi-depot, multi-region coordination.
Market Expansion
10,000+ vehicle fleet support. International expansion. Public transit EV scheduling. Autonomous fleet coordination. Carbon credit marketplace integration.
From Last-Mile Delivery to Public Transit
Last-Mile Delivery Fleets
Amazon, FedEx, UPS EV vans with 150-200 mile range on 250+ mile daily routes. Co-optimize routes with mid-day fast-charging stops. Eliminate range anxiety through predictive SOC management.
Municipal EV Bus Networks
Fixed-route transit with variable demand and tight schedules. Optimize overnight depot charging, en-route opportunity charging, and grid demand response during off-peak hours.
Utility Demand Response
Grid operators use DQR to coordinate fleet charging with renewable generation profiles. Defer $50B in distribution system upgrades. Revenue sharing model for fleet operators.
Ride-Hail & Autonomous Fleets
Waymo, Cruise, Uber EV fleets with dynamic demand patterns. Real-time re-optimization as ride requests arrive. Autonomous charging coordination without human drivers.
D-Wave Quantum Annealing for Coupled Combinatorial Optimization
Platform Architecture
Data Ingestion Layer
Vehicle telemetry (GPS, SOC, speed), charger status (OCPP), grid SCADA data, weather/traffic ML predictions, customer delivery windows
QUBO Formulation Engine
Coupled routing-charging-grid QUBO matrices, dynamic constraint weighting, problem decomposition for QPU capacity, rolling horizon optimization
Quantum Optimization Core
D-Wave Advantage (5000+ qubits, Pegasus), topology-aware minor embedding, chain strength optimization, hybrid quantum-classical solver
Edge Execution Layer
Depot edge nodes, real-time route push to vehicles, OCPP charger coordination, grid demand response signals, continuous re-optimization
Integration & Capabilities
- D-Wave Ocean SDK with QUBO/Ising formulation
- REST/gRPC APIs for Samsara, Geotab, Motive, Verizon Connect
- OCPP 2.0.1 for ChargePoint, ABB, Siemens, Tesla
- OpenADR 2.0b for utility demand response
- Real-time battery degradation modeling
- Time-of-use pricing optimization
- Carbon emission minimization objective
- Multi-depot, multi-region coordination
- 50 to 10,000+ vehicle fleet support
- Sub-minute re-optimization on disruption events
Two-Sided SaaS Platform: Fleet Operators + Grid Operators
Fleet SaaS
Per-vehicle subscription for quantum-optimized routing and charging. Scales with fleet size. 15-20% operating cost savings pays for itself 5-10x.
Grid Revenue Share
Revenue share on demand response payments from grid operators. Fleet charging coordination generates grid operator value that funds fleet operator discounts.
Enterprise Platform
Enterprise license for 1000+ vehicle fleets. Custom QUBO formulations, dedicated QPU allocation, on-premise edge deployment, white-label integration.