A5030103230- Electric Vehicles and Infrastructure
- Advanced Battery Technologies Research
- Electric and Hybrid Vehicle Technologies
- Vehicle emissions and performance
- Hybrid Renewable Energy Systems
- Transportation and Mobility Innovations
- Advancements in Battery Materials
- Railway Systems and Energy Efficiency
- Engineering Applied Research
- Real-time simulation and control systems
- Energy, Environment, and Transportation Policies
- Underground infrastructure and sustainability
- Traffic control and management
- Transportation Planning and Optimization
- Advanced Manufacturing and Logistics Optimization
National Renewable Energy Laboratory
2021-2023
As electric vehicle penetration increases, charging is expected to have a significant impact on the grid. Electric stations will greatly affect building site's power demand, especially with onset of fast levels as high 350 kW per charger. Here, we assess how would retail big box grocery store, exploring numerous station sizes, levels, and utilization factors in various climate zones seasons. We measure effect by assessing changes monthly peak electricity usage, annual bill, computed using...
Extreme DC fast charging could be competitive with the internal combustion engine refueling experience and enable longer-distance travel, but have high capital costs extremely variable, high-power demands. For low-wait, 200-miles-in-10-minutes XFC stations behind-the-meter systems (BTMS), optimal break-even levelized of (LCOC) are examined across 96 scenarios. LCOC is simulated via synthetic XFC-capable EV loads, machine-learned battery life models from testing data, nonlinear controls...
<div class="section abstract"><div class="htmlview paragraph">As connected and automated vehicle technologies emerge proliferate, lower frequency trajectory data is becoming more widely available. In some cases, entire fleets are streaming position, speed, telemetry at sample rates of less than 10 seconds. This presents opportunities to apply powertrain simulators such as the National Renewable Energy Laboratory’s Future Automotive Systems Technology Simulator model how advanced...
Developing EnStore Tool for Multi-Model Optimization tool will use REOPT, SAM, and EnergyPlus/OpenStudio:• REopt: REopt uses a mixed-integer linear programming (MILP) approach to recommend the optimal mix of renewable energy, conventional generation, energy storage technologies meet cost savings, resilience, performance goals.This MILP requires simplified, linearized models • SAM: System Advisor Model (SAM) is techno-economic software model that can many types systems, including photovoltaic...
Developing EnStore Tool for Multi-Model Optimization tool will use REOPT, SAM, and EnergyPlus/OpenStudio:• REopt: REopt uses a mixed-integer linear programming (MILP) approach to recommend the optimal mix of renewable energy, conventional generation, energy storage technologies meet cost savings, resilience, performance goals.This MILP requires simplified, linearized models • SAM: System Advisor Model (SAM) is techno-economic software model that can many types systems, including photovoltaic...