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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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4 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Electrified autonomous freight benefit analysis on fleet, infrastructure and grid leveraging Grid-Electrified Mobility (GEM) model

Fast-growing freight activities over the decades have become one of the major contributors to air pollution, leading to many efforts in freight decarbonization and electrification. However, the development of freight electrification is slow due to technological uncertainty, slow charging, high capital cost, etc. This paper analyzes the potential impact and benefit of heavy-duty vehicle (HDV) electrification and automation on fleet cost, infrastructure cost, the electricity grid, and environmental outcomes. In this work, we extended the vehicle electrification benefit analysis tool: Grid-Electrified Mobility (GEM) model, which had primarily been used to study light-duty passenger vehicles (LDVs), to analyze heavy-duty vehicle electrification. The extended model is derived for freight transportation electrification, and different freight electrification and automation adoption scenarios were analyzed. We find that the increased penetration of automated electric freight fleets within other types of electrified freight fleets from 1% to 99% will result in an overall cost reduction of 18.2%, fleet size reduction of 20.4%, and lower peak load reduction of 14.3%.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Optimizing workplace charging facility deployment and smart charging strategies

This study introduces a workplace charging (WPC) optimization model that maximizes the total satisfied electric miles of employees’ plug-in electric vehicles, subject to a given annual budget. The model optimizes both planning decisions of charger number and power levels and operation decisions of charging spot assignment and charging schedule for the given temporal distribution of charging demands and varied electricity prices. Results of experiments based on national average travel data indicate that the actual WPC strategy varies by budget level. Through optimization, the strategy could reduce impacts of the varied electricity price by shifting charging schedules to periods when electricity prices are low. Also, the model is expanded to study the trade-off between providing WPC and addressing consequence of degraded charging service by including the per-mile shadow cost of unsatisfied charging demand. Finally, we observe that their relative competitiveness mainly depends on the actual shadow cost of WPC.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2010

Smart Power Supply for Battery-Powered Systems

A power supply for battery-powered systems has been designed with an embedded controller that is capable of monitoring and maintaining batteries, charging hardware, while maintaining output power. The power supply is primarily designed for rovers and other remote science and engineering vehicles, but it can be used in any battery alone, or battery and charging source applications. The supply can function autonomously, or can be connected to a host processor through a serial communications link. It can be programmed a priori or on the fly to return current and voltage readings to a host. It has two output power busses: a constant 24-V direct current nominal bus, and a programmable bus for output from approximately 24 up to approximately 50 V. The programmable bus voltage level, and its output power limit, can be changed on the fly as well. The power supply also offers options to reduce the programmable bus to 24 V when the set power limit is reached, limiting output power in the case of a system fault detected in the system. The smart power supply is based on an embedded 8051-type single-chip microcontroller. This choice was made in that a credible progression to flight (radiation hard, high reliability) can be assumed as many 8051 processors or gate arrays capable of accepting 8051-type core presently exist and will continue to do so for some time. To solve the problem of centralized control, this innovation moves an embedded microcontroller to the power supply and assigns it the task of overseeing the operation and charging of the power supply assets. This embedded processor is connected to the application central processor via a serial data link such that the central processor can request updates of various parameters within the supply, such as battery current, bus voltage, remaining power in battery estimations, etc. This supply has a direct connection to the battery bus for common (quiescent) power application. Because components from multiple vendors may have differing power needs, this supply also has a secondary power bus, which can be programmed a priori or on-the-fly to boost the primary battery voltage level from 24 to 50 V to accommodate various loads as they are brought on line. Through voltage and current monitoring, the device can also shield the charging source from overloads, keep it within safe operating modes, and can meter available power to the application and maintain safe operations.

Krasowski, Michael J.↗
DOE OSTI2021

Energy and Emission Prediction for Mixed-Vehicle Transit Fleets Using Multi-task and Inductive Transfer Learning

Public transit agencies are focused on making their fixed-line bus systems more energy efficient by introducing electric (EV) and hybrid (HV) vehicles to their fleets. However, because of the high upfront cost of these vehicles, most agencies are tasked with managing a mixed-fleet of internal combustion vehicles (ICEVs), EVs, and HVs. In managing mixed-fleets, agencies require accurate predictions of energy use for optimizing the assignment of vehicles to transit routes, scheduling charging, and ensuring that emission standards are met. The current state-of-the-art is to develop separate neural network models to predict energy consumption for each vehicle class. Although different vehicle classes’ energy consumption depends on a varied set of covariates, we hypothesize that there are broader generalizable patterns that govern energy consumption and emissions. In this paper, we seek to extract these patterns to aid learning to address two problems faced by transit agencies. First, in the case of a transit agency which operates many ICEVs, HVs, and EVs, we use multi-task learning (MTL) to improve accuracy of forecasting energy consumption. Second, in the case where there is a significant variation in vehicles in each category, we use inductive transfer learning (ITL) to improve predictive accuracy for vehicle class models with insufficient data. As this work is to be deployed by our partner agency, we also provide an online pipeline for joining the various sensor streams for fixed-line transit energy prediction. Here, we find that our approach outperforms vehicle-specific baselines in both the MTL and ITL settings.

97 MATHEMATICS AND COMPUTING↗