FASTSim: Future Automotive Systems Technology Simulator
The Future Automotive Systems Technology Simulator (FASTSim™) provides a simple way to compare powertrains and estimate the impact of technology improvements on light-, medium-, and heavy-duty vehicle efficiency, performance, cost, and battery life.
This extremely fast simulation tool features a streamlined user interface and can rapidly perform a variety of tasks using basic computing resources:
- <0.1 second to simulate second-by-second standard duty cycles
- <10 seconds to estimate vehicle efficiency, fuel economy, acceleration, battery life, and cost
- <5 minutes to perform powertrain comparisons of efficiency and cost.
FASTSim models a variety of vehicle powertrains and fuel converter types:
- Conventional vehicles — spark ignition, Atkinson, diesel, and hybrid diesel
- Electric-drive vehicles — hybrid, plug-in hybrid, and all-electric
- Hydrogen fuel cell vehicles.
The default model includes a variety of vehicles and duty cycles and an option for adding additional vehicles and custom cycles:
- Packaged with more than 20 vehicles
- User interface enables addition of more vehicles
- Includes standard U.S. drive cycles as well as European and Japanese cycles.
Download FASTSim
FASTSim is available for download in Microsoft Excel and Python formats.
Register for the Excel Version of FASTSim
Easy to Use, Enables Flexible Analysis
The Excel version of FASTSim features an easy-to-use, interactive interface that simplifies the process of importing new vehicle data and custom drive cycles. In addition to calculating energy consumption, the Excel version enables life-cycle cost comparisons, battery life comparisons, component sizing tradeoffs, design-of-experiment inquiries, and more.
Register for the Python Version of FASTSim
Compatible with Large Datasets
The Python version of FASTSim easily integrates with large duty-cycle databases. Pairing with the Transportation Secure Data Center, for example, facilitates multi-day simulations incorporating vehicle dwell times. When paired with geospatial cycles, FASTSim helps users draw conclusions at the regional level by incorporating such factors as temperature, roadway characteristics, or driving behavior.
Customizable for More Specific Analysis
The component models in the Python version are easily expandable and can be adjusted to incorporate additional energy consumption impacts. The model’s flexibility and speed simplify A/B comparisons for vehicle powertrains, control strategies, or technologies. Its large number of included vehicles and computational efficiency make calculating impacts at the fleet-level easy.
Feedback
While NLR does not provide technical support for FASTSim, we welcome your feedback on the tool. Please email your feedback or report any problems to [email protected].
Publications
The following publications provide examples of how FASTSim can be used to evaluate real-world vehicle efficiency, compare powertrains, assess component improvements, or conduct other types of vehicle analyses.
Empowering Electric Bus Deployment with Standardized Transit Data, Transportation Research Record: Journal of the Transportation Research Board (2026)
Drive Cycles, Battery Pack Design, and Usage Considerations for Long-Haul and Regional-Haul Electric Trucks, Journal of Power Sources (2026)
PV System Technology Considerations for PV-Powered Passenger Vehicles, International Energy Agency Report (2026)
Performance Analysis of a Small-Sized Hybrid Vehicle on a Real-World Inclined Route Under WLTP, ARTEMIS, and NEDC Drive Cycles, Fuel Cells (2026)
Linear Programming Formulation for Planning of Future Model-Year Mix of Electrified Powertrains, World Electric Vehicle Journal (2026)
Impact Assessment of Battery-Electric HDVs Charging Loads on the Transmission and Distribution System in Iceland, Applied Energy (2026)
Electric Vehicle Energy Consumption Modeling Using Real-World Driving Data: System Identification vs. Machine Learning, IEEE Transactions on Intelligent Vehicles (2026)
Assessment of Heavy-Duty Fueling Methods and Components—Modeling and Analysis, NLR Technical Report (2025)
Thermal Intelligence Big Data and AI for Sustainable Battery and Cabin Heat Management in Electric Vehicle, American Journal of Engineering and Technology (2025)
Impact of EV Charging Stations Reliability, Resilience, and Location on EV Adoption, Continuing Education and Development Report (2025)
Green Hydrogen for Road Transport in Western Australia, Future Energy Exports Cooperative Research Centre Report (2025)
A Methodological Framework for Developing Novel Vehicle Concepts Based on Quantified End-Customer Techno-Economic Criteria, Technical University of Liberec Dissertation (2025)
Comprehensive Framework for Energy Consumption Estimation in Electric Vehicles, IEEE Transactions on Intelligent Transportation Systems (2025)
Advancements in AI-Powered Electric Vehicle Routing: Multi-Constraint Optimization and Infrastructure Integration Approaches for Evolving EVs-A Survey, IEEE Access (2025)
The CanBikeCO Full Pilot: Long-Term Results and Analysis from an E-Bike Program in Colorado, USA, International Journal of Sustainable Transportation (2025)
HD ADOPT: Heavy-Duty Vehicle Choice Model Documentation, NLR Strategic Partnership Project Report (2025)
Route Energy Prediction (RouteE) Powertrain Validation Report, NLR Technical Report (2025)
Optimising Fast-Charging Infrastructure for Long-Haul Electric Trucks in Remote Regions Under Adverse Climate Conditions, eTransportation (2025)
Combining Statistical and Machine Learning Methodologies in Energy Consumption Forecasting for Electric Vehicles, Preprints (2025)
Deep Q-Learning Based Optimal Energy Management of a Plug-in Hybrid Electric Vehicle, ASME International Mechanical Engineering Congress (2025)
Research on the Correlation Mechanism Between Complex Slopes of Mountain City Roads and the Real Driving Emission of Heavy-Duty Diesel Vehicles, Sustainability (2025)
How To Cite FASTSim
If you use FASTSim for work described in a publication, please notify us and include this citation in your publication:
Brooker, A., Gonder, J., Wang, L., Wood, E. et al., "FASTSim: A Model To Estimate Vehicle Efficiency, Cost, and Performance," SAE Technical Paper 2015-01-0973, 2015, doi:10.4271/2015-01-0973.
More Information
For more information about FASTSim, refer to these seminal publications:
FASTSim: A Model to Estimate Vehicle Efficiency, Cost, and Performance, SAE World Congress (2015)
FASTSim Validation Report, NLR Technical Report (2021)
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Last Updated April 17, 2026