Search NASA⌕ Search

Engineering topics

Smart, John G

Publications and source records attributed to Smart, John G.

5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Slide Decks for EV@S Stakeholder Meeting at INL September 2024

These slides were used at the Electric Vehicles at Scale National Lab Consortium, Stakeholder Meeting hosted at INL in September 2024. The slides are now requested to be made available publicly for reference. All slides review projects that are already in the public domain and their information is intended for public release. They are not from sensitive topics or areas with any restrictions. A few projects are overviewed in detail and some slides were just an aid to the breakout sessions that were conducted with stakeholders and lab staff.

25 ENERGY STORAGE↗
DOE OSTI2024

Statistical Estimation of EV Driver Charging Behavior and Influential Factors

INL received data collected via telematics from battery electric vehicles (BEVs), and these vehicles were owned by retail customers who had entered into a telematics user agreement. The goal of analyzing these data was to develop mathematical models to characterize how different sets of BEV drivers use charging infrastructure at home and away from home (i.e., public charging) and quantify how various factors influence BEV drivers’ decision to charge and use available infrastructure. The data used in this analysis are unique because they provide real world BEV driving and charging behavior at the individual driving and parking event level. In this study we seek to leverage this data to quantify BEV charging and driving metrics to help inform models that predict quantities like the specific times when loads are imposed on the electrical grid due to BEV charging. Most models that have been developed to predict electrical grid load due to BEV charging, use simulations of BEV driving events and rely on assumptions such as every vehicle charges every night. Using a statistical modelling framework, we seek to investigate BEV charging behavior and quantitatively assess these common assumptions of BEV charging behavior.

33 - ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

NRIC Strategy for Advancing Nuclear Integrated Energy Systems

Integrated energy systems (IES) combine low-carbon energy sources to meet demand for clean energy in multiple forms across multiple sectors of economy. IES create opportunities to cost-effectively dispatch generation resources to balance electricity supply and demand. In addition to producing electricity, IES increase the value of non-emitting energy sources by integrating generation technologies with industrial processes to efficiently produce commodities that benefit the manufacturing, industrial processing, and transportation industries. Nuclear energy technology is key to enabling these benefits. Advanced nuclear reactors capable of producing high-quality heat, electricity, and radiation, will play a foundational role in IES of the future.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Integrated Dispatching and Charging Management of an Autonomous Electric Vehicle Ride-Hailing Fleet

Electrification and autonomous driving are two important trends in transportation systems. The convergence of these two technologies will introduce opportunities to improve transportation systems' operation and energy efficiency. One potential application is the commercial ride-hailing fleet with autonomous electric vehicles (AEVs). In order to harvest promising benefits from introducing AEVs into ride-hailing fleets, some unavoidable challenges will need to be resolved to ensure the fleets operates functionally and efficiently. This paper discusses the challenges of dispatching AEVs and their interactions with charging infrastructure. An integrated decision making framework for dispatching and charging has been designed using a system optimization approach to study the AEVs' management within the period when they drop off passengers and pick up the next passengers. Its potential fleet-wide benefits have been illustrated by comparing operations under a heuristic approach. A simulation platform has been designed to test different decision making strategies for the ride-hailing AEV fleets operational performance. Using this platform, detailed case studies have been performed with different fleet sizes, dispatching strategies, and charging infrastructure network settings. Comprehensive analyses from various aspects have been conducted to understand the AEVs' fleet operation performance, (e.g., zero occupancy vehicle miles traveled, successfully served ratio of ride-hailing requests, fleet vehicle charging downtime, and charging infrastructure utilization). Results have provided a deep understandings on operation's dynamics under various fleet system configurations and also have demonstrated advantages of the optimization-based approach for the AEV fleet management. Studies in this paper inform better designs on the future of sophisticated management strategies and charging infrastructure to support ride-hailing AEV fleet operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗