Advenno built FleetCharge, a smart EV fleet charging platform with AI-optimized scheduling, demand response, and V2G capability. Charging costs dropped 38%, peak demand charges fell 61%, and 99.4% vehicle availability was maintained across 420 EVs.
The Challenge
VoltFleet Logistics had committed to converting its 420-vehicle delivery fleet from diesel to electric over three years — a $28M investment driven by sustainability goals, regulatory pressure, and the promise of lower per-mile operating costs. The first 180 vehicles were deployed across 8 depots with 180 chargers, but the charging infrastructure quickly became the operation's biggest bottleneck and cost problem. Without intelligent management, drivers plugged vehicles in simultaneously upon returning from afternoon routes, creating massive demand spikes between 4-7 PM — precisely when electricity rates were highest. Peak demand charges, billed based on the highest 15-minute demand interval each month, accounted for 42% of the total electricity bill. A single day's charging spike set the demand charge for the entire month. When chargers were occupied, vehicles queued for hours, causing late departures for evening and overnight shifts. Route planners had no visibility into battery state-of-charge until vehicles physically returned, making it impossible to optimize routes for charging needs. The economic model that justified the fleet electrification had projected energy costs of $0.04 per mile; actual costs were running $0.056 per mile — 40% higher — primarily due to peak demand charges. If the cost trajectory continued, the 10-year total cost of ownership case for electric vehicles would not meet the breakeven threshold against diesel, undermining the entire electrification strategy.
- 42% of electricity bill from peak demand charges caused by simultaneous unmanaged charging
- Vehicle queuing for occupied chargers delaying shift departures and disrupting delivery schedules
- No visibility into battery state-of-charge until vehicles returned to depot
- Energy cost per mile 40% above projections threatening the economic case for electrification
- First-come-first-served charging with no optimization for rates, demand thresholds, or schedules
- $1.8M annual charging costs with no demand response or grid participation revenue
Our Solution
Advenno built FleetCharge as a three-layer platform: vehicle intelligence, charging orchestration, and grid interaction. The vehicle intelligence layer connects to each EV's telematics system to monitor real-time state of charge, estimated range, location, and battery health. Combined with route scheduling data, the platform knows exactly how much energy each vehicle needs and when it must be ready. The charging orchestration engine creates optimal charging schedules for all 420 vehicles across 180 chargers, considering electricity rate schedules (time-of-use tiers), demand charge thresholds (keeping the 15-minute peak below the contracted limit), charger capacity constraints, vehicle departure times, required ranges, and battery conditioning needs for cold weather. The engine staggers charging to flatten the demand curve — rather than 120 vehicles charging simultaneously at 4 PM, it might charge 30 immediately (those departing soonest), defer 60 to the overnight off-peak window, and schedule the remaining 30 during the early-morning shoulder rate period. The grid interaction layer participates in utility demand response programs — reducing or pausing charging during grid stress events in exchange for revenue credits — and implements vehicle-to-grid (V2G) capability where parked vehicles with sufficient charge export energy back to the grid during peak pricing periods, earning additional revenue. A fleet manager dashboard provides real-time visibility into charging status, energy costs, vehicle readiness, and grid participation revenue across all 8 depots.
- AI charging orchestration optimizing across rate schedules, demand thresholds, vehicle needs, and charger capacity
- Real-time vehicle state-of-charge monitoring and range prediction from telematics integration
- Demand curve flattening that reduced peak demand charges by 61%
- Utility demand response participation earning revenue credits during grid stress events
- Vehicle-to-grid (V2G) energy export during peak pricing turning fleet into distributed energy asset
- Fleet manager dashboard with charging status, costs, readiness, and grid revenue across 8 depots
- Cold weather battery conditioning scheduling ensuring optimal range in all conditions
Our Approach
Energy & Operations Analysis
Analyzed 6 months of electricity billing data, charger utilization logs, route schedules, and vehicle telematics to map the complete energy profile of the fleet. Identified that 61% of total energy costs were driven by demand charges from unmanaged simultaneous charging — the single largest optimization opportunity.
Charging Algorithm Development
Developed the multi-objective optimization algorithm that balances vehicle readiness, energy cost, demand charge management, battery health, and grid participation. The algorithm was backtested against 6 months of actual operations, projecting 34% cost reduction — conservative compared to the 38% achieved in production.
Utility Partnership
Negotiated demand response enrollment and V2G interconnection agreements with the local utility serving all 8 depots. Established the technical protocols for demand response signal reception, V2G power export metering, and settlement calculations for grid services revenue.
IoT Integration
Integrated with all 180 chargers (ChargePoint and ABB) and all 420 vehicles (Rivian and BrightDrop) telematics systems via APIs. Built the real-time data pipeline processing charging status updates every 30 seconds and vehicle telemetry every 60 seconds across the fleet.
Phased Deployment
Deployed to the 2 highest-cost depots first, demonstrating 41% cost reduction in the first billing cycle. This immediate proof point secured enthusiastic support for the remaining 6 depots, which were deployed over the following 4 weeks.
The Results
FleetCharge delivered transformational economics for VoltFleet's electrification program. Total charging costs dropped 38%, bringing energy cost per mile from $0.056 to $0.035 — actually below the original $0.04 projection and firmly establishing the economic advantage of electric over diesel. Peak demand charges were reduced by 61% through intelligent load management that kept 15-minute demand peaks below contracted thresholds. Vehicle availability reached 99.4% — virtually every vehicle was fully charged and ready for its scheduled departure, eliminating the queuing delays that had disrupted delivery schedules. The demand response program generated $180K in annual revenue credits by reducing or pausing charging during 47 grid stress events throughout the year — events that typically occurred during summer afternoon peaks when VoltFleet vehicles were out on routes and chargers were idle anyway. V2G participation generated an additional $140K by exporting stored energy during the highest-price grid intervals, turning the parked fleet into a revenue-generating distributed energy asset. Combined, these grid participation revenues offset 17% of the remaining charging costs. The fleet manager dashboard provided real-time visibility that transformed planning: route schedulers could see projected state-of-charge for every vehicle, enabling range-aware route optimization that eliminated the 2.3% of routes previously disrupted by unexpected low-battery situations. VoltFleet used the FleetCharge economics to accelerate the remaining fleet conversion, moving the completion date forward by 14 months.
Return on Investment
Technologies Used
Integrations
FleetCharge saved our electrification strategy. We went from questioning whether EVs could match diesel economics to proving they're significantly cheaper — and earning revenue from the grid on top of it. Every vehicle is charged and ready when it needs to be, and our drivers haven't noticed any change except quieter trucks.
Project Gallery
Lessons Learned
- Deploying to highest-cost depots first created undeniable proof points that secured enthusiasm for fleet-wide rollout
- Demand charge reduction was the largest single cost lever — addressing the 15-minute peak was more impactful than time-of-use optimization
- V2G participation required careful coordination with the utility and clear contractual terms for energy export settlement
- Route planners needed real-time charge visibility to trust the system — building confidence took 2-3 weeks of consistent accuracy
Summary
Advenno built FleetCharge, a smart EV fleet charging platform for 420 electric delivery vehicles across 8 depots. AI-optimized scheduling, demand response, and V2G capability reduced charging costs 38%, cut peak demand charges 61%, and generated $320K in annual grid revenue while maintaining 99.4% vehicle availability.
Key Takeaways
- AI charging orchestration reduced peak demand charges 61% by flattening the simultaneous charging spike
- V2G and demand response generated $320K in annual revenue — turning a cost center into a partial revenue source
- Backtesting against 6 months of actual data before deployment built confidence in projected savings
- Real-time state-of-charge visibility enabled range-aware route optimization eliminating 2.3% route disruptions
- Deploying to the 2 highest-cost depots first demonstrated 41% savings in the first billing cycle
Frequently Asked Questions
Key Terms
- Demand Charge
- A component of commercial electricity billing based on the highest 15-minute average power demand during the billing period — a single spike sets the charge for the entire month.
- Vehicle-to-Grid (V2G)
- Technology enabling electric vehicles to export stored energy back to the power grid during peak demand, generating revenue for the fleet operator while supporting grid stability.
- OCPP
- Open Charge Point Protocol — an open communication standard between EV charging stations and central management systems, enabling interoperability across different charger brands.
Facts & Statistics
Sources & Citations
- NREL: Fleet Electrification Study
- Rocky Mountain Institute: V2G Economics
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