Real Data, Real Impact: How NREL Proves Large‑Scale Fleet Logging Works 🚚

At HoneyRuns, we believe in data that drives real change - live, large-scale, real-world data about how vehicles actually operate in the field. The National Renewable Energy Laboratory (NREL) followed the same principle: they logged vehicle data across hundreds of real-world commercial vehicles to show that collecting and analyzing large-scale data works.

1. Logging Every Second: How It Started

NREL outfitted active fleets—delivery vans, school buses, tractors, refuse and transit trucks—with 1 Hz data loggers. Each second, these devices recorded GPS coordinates, speed, engine data, fuel use, emissions, and more from vehicle CAN-bus systems Nrel Report.

Why this matters:
Minute-by-minute data reflects real operations—the stop-and-go, idling at lights, depot waits—not lab-based highways.

2. Cleaning the Data: From Log Files to Drive-Days

With raw data streaming in, NREL developed a pipeline to:

1. Clean up bad GPS points and fill gaps
2. Split data into “drive-day” segments
3. Calculate stats like miles/day, idling %, fuel/day, braking energy, and average speed Nrel Report Afdc Report.

This gave them structured, quantitative summaries — hundreds of metrics per vehicle per day.

3. Scaling Up: Hundreds of Vehicles, Millions of Data Points

By 2016, the program included:

• ~1,700 vocational vehicles
• Covering ~11.5 million miles
• Logging over 500,000 hours of data from 913 unique vehicles Afdc Report Nrel Report

That’s a breadth of driving across types, geographies, and duties.

4. Pattern Recognition: Clusters & Cycles

Instead of treating all vehicles the same, they grouped drive-days into clusters — like urban, mixed, highway use. From each group, they extracted “representative cycles” for lab testing.

Using NREL’s DRIVECAT tool, they generated:

• Urban refuse truck cycles (~11 mph, 30 min)
• Class 6 EV cycles (~16 mph, 20 mi)
• Port drayage “creep” cycles (0.7 mph avg) Nrel Report

These beat generic test cycles—because they match real-world use.

5. Idling: A Major Cost Driver

NREL measured real-world idling — time engine on but vehicle stationary — across fleets and identified:

• % of a typical day spent idling
• Idle fuel burned (gallons/day → gallons/year)
• Deviations from lab-based assumptions Afdc Report Afdc Report Nrel Report

Conclusion: fleets burn a lot of fuel just waiting. Tracking idling is low-hanging fruit for savings.

6. Braking Data: The Hybrid Opportunity

By measuring kinetic intensity and brake-energy, NREL could estimate potential benefits of regenerative braking and hybrid systems Nrel Report.
High stop-start routes = high potential for energy recovery.

7. Turning Data into Action: Reporting & Insights

For each fleet vehicle, NREL summarized:

• Total & daily miles, fuel, CO₂ emissions
• Idling %, braking energy, average speed
• Estimated annual use & emissions
• Dwell times (to assess EV charging windows)
• A simple “EV candidate score” based on usage patterns Afdc Report

Even without flashy dashboards, this data informed real operational decisions.

8. Real-World Validation Drives Better Tech

Rather than guessing, OEMs and researchers used NREL’s cycles for lab tests and simulations. These cycles have average speeds, stop frequencies, and brake-intensity rooted in real data. Result: technology tested how it’s actually used Afdc Report Afdc Report.

9. Policy and Planning Influence

NREL’s idling and start-cycle info improved EPA’s MOVES emissions models, which previously relied on lab-based idle assumptions Docs Report.

Representative cycles also shaped GHG standards for vocational vehicles Afdc Report.

10. Conclusion: Large-Scale Data Works

NREL’s step-by-step process shows how collecting real-world data at scale leads to real results:

• Collect: 1 Hz CAN+GPS across hundreds of vehicles
• Process: clean, segment, compute metrics per vehicle-day
• Analyze: cluster usage, extract drive cycles, measure idle/brake stats
• Report: vehicle-level summaries, annual projections, fuel/emission insights
• Validate: lab tests, emissions models, EV scoring
• Improve: vehicles perform better, policies improve, fleets run smarter

The verdict? Large-scale, real-world data → actionable insights → real improvements.

11. HoneyRuns: Following the Same Path

HoneyRuns collects live data from your fleet and processes it into actionable analytics and performance benchmarks — just like NREL Honeyruns. Then, we take it a step farther - we execute the action for you. All within one simple dashboard.

That’s the same approach NREL takes — only now it's applied to your fleet.

📌 Final Takeaway

Big data matters — but only real-world, large-scale data. NREL showed that by logging seconds of actual driving across hundreds of vehicles, analyzing patterns, and feeding insights back into lab testing, policy, and operations, they unlocked better efficiency, better tech, and better planning.

HoneyRuns does the same. We record, analyze, benchmark, and help fleets improve. That’s fool proof data that works at scale.

Ready to transform your fleet operations? Start your free trial or schedule a demo to see HoneyRuns in action.