EVI-InMotion: Electric Vehicle Infrastructure – In Motion Tool
NREL's Electric Vehicle Infrastructure – In Motion (EVI-InMotion) tool is used for planning, optimizing, and analyzing the feasibility of charging electric vehicles (EVs) while driving on electric roads.
This versatile tool is applicable to the full range of vehicle classes—light-, medium-, and heavy-duty—as well as various in-motion charging technologies—inductive, capacitive, and conductive. EVI-InMotion uses high-resolution travel data along with detailed information about existing road networks to optimize system design parameters and evaluate performance.
- Optimizing dynamic charger system parameters for a given vehicle and drive cycle, with a focus on electric road coverage, charger power, charger location, charger length at each location, onboard battery size, and the number of power receivers on the EV
- Evaluating the impact of dynamic chargers on EV efficiency, driving range, and charging time
- Performing feasibility analyses for large-scale deployments—at the city or state level—of dynamic chargers on public roadways
- Exploring the impact of dynamic chargers on light- and medium-duty EV intercity and intracity travel as well as heavy-duty EV local, regional, and long-haul travel.
How It Works
EVI-InMotion incorporates several models and algorithms.
Vehicle Powertrain Model – Predicts EV battery power, energy, and state of charge while considering vehicle system parameters, driving behavior, and regenerative braking. Based on NREL’s Future Automotive Systems Technology Simulator.
Cost Model – Accounts for charger components including power converters, materials, and installation costs as well as vehicle components such as batteries and onboard electronics.
Dynamic Inductive Charger Power Model – Estimates the power an EV receives via the electric road. One segment of charger is modeled for power as a function of charger dimensions and alignment. Several segments are stacked together according to the desired electric road length.
Automatic Placement Algorithm – Allocates the number and location of charger segments. Drawing on road and travel data, the algorithm places the system based on existing road networks in the United States.
Automatic Search Algorithm – Solves a single- or multi-objective planning optimization problem, identifying the best combination of charger system parameters (e.g., power level, roadway coverage, location, length at each location, etc.) and vehicle system parameters (e.g., battery size and number of power receivers on the EV). Such optimization aims to minimize overall system costs—specifically, charger and vehicle component costs—while satisfying driving range and charging time requirements.
NREL developed two versions of EVI-InMotion—one for fixed-route applications and another for dynamic charging on public roadways.
In fixed-route applications, such as with circular and on-demand shuttle services and transit buses, EVI-InMotion employs representative drive cycles to optimize system design.
Dynamic Charging on Public Roadways
EVI-InMotion is also used to inform the design of large-scale dynamic charger deployments. Based on a real-world road network, EVI-InMotion draws on a large set of drive cycles to evaluate system performance. Optimized electric roadway system design is based on average energy models for vehicles and chargers along with performance and feasibility analyses for a given region.
The following publications provide detailed information about using EVI-InMotion for
planning, optimizing, and analyzing the feasibility of electric roads. View NREL’s full collection of related publications.
Planning Optimization for Inductively Charged On-Demand Automated Electric Shuttles Project at Greenville, South Carolina, IEEE Transactions on Industry Applications (2020)
Planning of In-Motion Electric Vehicle Charging on Freeways, IEEE Smart Grid Newsletter (2020)
SMART Mobility Advanced Fueling Infrastructure Capstone Report, U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Technical Report (2020)
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