Transportation and Mobility Research
EVI-InMotion: Electric Vehicle Infrastructure – In Motion Tool

EVI-InMotion: Electric Vehicle Infrastructure – In Motion Tool

NREL's Electric Vehicle Infrastructure – In Motion (EVI-InMotion) tool supports the planning, optimizing, and feasibility analysis of charging electric vehicles (EVs) while in motion using integrated charging technologies.

It applies 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 and detailed road network information to optimize system design parameters and evaluate performance.

Key Capabilities

EVI-InMotion optimizes dynamic charger system parameters for a given vehicle, speed trace, and road network, with a focus on charger coverage, charger power, charger location, charger length at each location, onboard battery size, and the number of power receivers on the EV.

These capabilities enable researchers to:

Evaluate the impact of dynamic chargers on EV efficiency, driving range, and charging time
Perform feasibility analyses for large-scale systems—at the city or state level—of dynamic chargers on public roadways
Explore 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
Evaluate tradeoffs between in-motion and stationary charging to inform analysis of grid interconnection impacts such as the location, distribution, size, and timing of loads.

How It Works

EVI-InMotion incorporates several models and algorithms.

Models

Vehicle Powertrain Model
Assesses EV battery power, energy, and state of charge while considering vehicle system parameters, driving behavior, and regenerative braking. Integrated with 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 electrified road. One charger segment is modeled for power as a function of charger dimensions and alignment. Several segments are stacked together according to the desired electrified road length.

Algorithms

Automatic Placement Algorithm
Allocates the number and location of charger segments. Drawing on road and travel data, the algorithm designs 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.

Applications

NREL developed two versions of EVI-InMotion—one for fixed-route applications and another for dynamic charging on public roadways.

Fixed-Route Applications

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.

Illustration showing two interlinked boxed areas, including an optimization layer at the top and an energy estimation layer at the bottom. The optimization layer features three connected sub-topics, including 1) evaluate fitness and constraints, 2) planning optimization algorithms, and 3) generate variables. A list under the planning optimization layer includes charge sustaining, state-of-charge limits, battery charging rate limit, and system cost model. The energy estimation layer features four connected sub-topics, including 1) travel data, 2) vehicle powertrain model, 3) charger power model, and 4) energy state-of-charge estimation. The travel data area links to the vehicle powertrain model (with speed and grade) as well as the charger power model (with route). The vehicle powertrain model and the charger power model connect to energy and state-of-charge estimation via a formula that includes “PE” at the positive (associated with the vehicle powertrain model) and “PC” at the negative (associated with the charger power model), resulting in Pbat as it connects to the energy state-of-charge area. Meanwhile, the energy state-of-charge area connects to the evaluate fitness and constraints area; and the generate variables area connects to the vehicle powertrain model (with battery capacity) as well as the charger power model (with track positions, track segments, and rated power).

Illustration showing the EVI-InMotion process flow for studying charging systems for fixed-route applications.

Dynamic Charging on Public Roadways

EVI-InMotion is also used to inform the design of large-scale dynamic charger systems. It draws on real-world road network data as well as extensive vehicle operating data—such as drive cycles from NREL's Fleet DNA clearinghouse of commercial fleet vehicle operating data—to evaluate system performance. The system design is optimized using energy models for vehicles and chargers, along with regional performance and feasibility analyses.

Illustration showing connectivity between various areas, including an original road network connecting (with road class, latitude, and longitude) to an allocation algorithm. Vehicle travel data also connects (with speed, route, road grade, and annual average daily traffic) to the allocation algorithm. Meanwhile, the allocation algorithm connects to an electrified road network, which connects (with locations) to a charger power model in an energy estimation layer. The energy estimation layer contains three components, including 1) charger power model, 2) energy state-of-charge estimation, and 3) vehicle powertrain model. The energy state-of-charge estimation area connects to the management and optimization algorithm, which connects to three areas, including the vehicle powertrain model (with battery capacity, charger power model (with length and rated power), and the allocation algorithm (with roadway coverage, number of dynamic/wireless power transfer per 300 miles).

Illustration showing the EVI-InMotion process flow for studying large-scale charging systems for primary roadways in a city or state.

Publications

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.

TCO Analysis Approach and Regional Analysis of dWPT for Class 8 Tractors , NREL Presentation (2024)

In-Route Inductive Versus Stationary Conductive Charging for Shared Automated Electric Vehicles: A University Shuttle Service, Applied Energy (2021)