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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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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Cosimulating Integrated Energy Systems With Heterogeneous Digital Twins: Matching a Connected World

Energy system integration promises in-creased resiliency and the unlocking of synergies, while also contributing to our goal of decarbonization. It is enabled by both old and new technologies, glued together with data and digital services. Hydrolyzers, heat pumps, distributed renewable generation, smart buildings, and the digital grid edge are all currently the subject of integration with the power system and the energy sector at large. To plan and operate such a multidisciplinary and multisectoral system properly, insight, tools, and expertise are all needed. Further, this is exactly where the state of the art fails to deliver: tools for integrated energy systems (IESs) are still in their infancy, and many times, even academia treats these sectors separately, producing experts in each of them but not across.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Homomorphic Encryption for Electrical Metering Aggregation: Protecting the Privacy of Building Tenants

Electrical meters are devices that measure consumer electricity usage. The data collected by these meters is necessary for utility billing and electrical grid management but can also be used to assess the environmental impact of buildings. Prior research has found that unprotected metering data could potentially be used to infer some information about the behaviors of building tenants by detecting changes in electricity usage. For example, a period of low electricity usage could suggest that the tenants are not in the building. As smart metering becomes more common, there is a growing need for data privacy protections for metering data that do not negatively impact the quality and availability of data used for energy management and billing applications. To identify potential solutions, we developed a Python-based data aggregation platform to analyze the potential efficacy of privacy-enhancing technologies for energy metering applications. This platform aggregates groups of metering sites into virtual buildings, which could potentially detach changes in electrical activity from individual tenants, making it more difficult to track the activity of a specific tenant. To further protect data during analysis, this project utilizes homomorphic encryption as part of its initial approach. Homomorphic encryption offers a means of protecting energy consumption data while permitting mathematical operations to be performed without the need to know the data contents. This allows for data to be processed into usable statistics without revealing energy consumption information. A series of homomorphic encryption libraries were evaluated to determine their applicability and limitations in the context of metering data. The use of these techniques may help to reassure consumers and encourage further adoption of smart grid infrastructure.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

A Blockchain-Enabled Sustainable Safety Management Framework for Connected Vehicles

The notion of an intelligent transportation system (ITS) aims to boost the performance of transportation networks, which has gained more and more traction in both academic and commercial circles. ITS is a constantly evolving vision that combines cutting-edge transportation approaches with new information, communication, computers, and other technology. ITS should discover consequence routes to enhance the sustainability, safety, and trustworthiness of the entire transportation system utilizing emerging technologies. In this paper, a sustainable safety management framework for connected vehicles is proposed by integrating blockchain. It introduces smart transportation equipment called an AI-enabled vehicle smart device (AVSD) for vehicular communications. AVSD can reduce energy consumption by decreasing the computational costs in vehicular communications. Smart contracts are used to identify vehicles automatically and establish secure communication among vehicles and emergency service stations (ESSs) like hospitals, police stations, and fire stations. The experiment results show that the proposed framework provides a communication environment for sustainable safety and security using the introduced smart transportation device. In conclusion, the proposed blockchain-enabled sustainable safety management framework has the potential to improve safety and sustainability in the transportation industry by creating a secure, decentralized, and transparent platform for managing safety data and promoting safe and sustainable driving behaviors.

42 ENGINEERING↗
DOE OSTI2023

Smart Manufacturing Pathways for Industrial Decarbonization and Thermal Process Intensification

Rapid decarbonization is fast becoming the primary environmental and sustainability initiative for many economic sectors. Industry consumes more than 30 % of all primary energy in the United States and accounts for nearly 25 % of all greenhouse gas (GHG) emissions. More than 70 % of energy consumed by the industrial sector is related to thermal processes, which are also the largest contributors of carbon emissions, overwhelmingly due to the combustion of fossil fuels. Thermal process intensification (TPI) seeks to dramatically improve the energy performance of thermal systems through technology pillars focusing on alternative energy sources and processes, supplemental technologies, and waste heat management. The impacts of TPI have significant overlap with the goals of industrial decarbonization (ID) that seeks to phase out all GHG emissions from industrial activities. Emerging supplemental technologies such as smart manufacturing (SM) and the industrial internet of things (IoT) enable significant opportunities for the optimization of manufacturing processes. Combining strategies for TPI and ID with SM and IoT can open and enhance existing opportunities for saving time and energy via approaches such as tighter control of temperature zones, better adjustment of thermal systems for variations in production levels and feedstock properties, and increased process throughput. Data collected by smart processes will also enable new advanced solutions such as digital twins and machine learning algorithms to further improve thermal system savings. Herein, this paper examines the individual pathways of TPI, ID, and SM and how the combination of all three can accelerate energy and GHG reductions.

42 ENGINEERING↗
DOE OSTI2022

ReachNow EV Driving Data From Seattle, WA, Portland, OR, and New York, NY

ReachNow provided Idaho National Laboratory (INL) with a dataset describing approximately 49,000 trips taken by customers and employees in approximately 100 BMW i3 EVs operating in ReachNow's free-floating car-sharing fleets in Seattle, WA, Portland, OR, and New York, NY between May 2016 and February 2017. Data fields include vehicle rental period start and end timestamps, the location where vehicles were parked at the start and end of rental periods, and distance driven during rental periods. A field categorizing the user during each rental period is also included. This field makes it possible to identify when vehicles were rented by customers and when vehicles were driven by fleet management team employees to reposition, charge, or service the vehicles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

EVs@Scale Next-Gen Profiles - EV Profile Capture 2023

As part of the U.S. DOE EVs@Scale consortium Next-Gen Profiles (NGP) project, the profile capture of production electric vehicles undergoing high power charging (HPC) is conducted over a wide range of conditions to explore variance and performance. Charge session parameters are collected from both the electric vehicle (EV) and electric vehicle supply equipment (EVSE) at a rate of 10Hz and entered into a time-series database for analysis. These charge profiles are captured under nominal and off-nominal conditions, exploring the impact of battery state of charge (SOC), battery temperature, vehicle condition, smart charge management (SCM), and EVSE limitations. Nominal conditions are defined to be ideal conditions that should transfer the maximum allowable energy in the minimum possible amount of time. Nominal condition profiles are compared across EVs to characterize state-of-the-art EV charging performance against one another. Off-nominal condition profiles are compared against its nominal condition profile counterpart to highlight the variance across less desirable starting conditions within a single EV. Under nominal conditions, most EVs can achieve the original equipment manufacturer (OEM) rated peak-power and charge times. Peak power across EVs has variance due to the vehicle design, battery topology, charging strategy, etc. These 10-100% nominal preconditioned charge profiles across 13 EVs (11 light-duty (LD), 2 heavy-duty (HD)) were used for analysis in power curve analysis, power distribution, SOC and range comparison, thermal impacts of current draw, battery pack size and energy charged, and ramp rates. Power curve analysis show the uniqueness in power vs time curves across all EVs, breaking down the features of a typical profile and where variation is typically seen. Power distribution results showed that over 50% of charge time is spent below 50kW and only 12.1% is spent above 200kW. SOC and range performance yielded different top performing EVs when exploring goals of performance from SOC and range gained after 10-min (EV2) and 20-min (EV8), and time-to-achieve 80% SOC (EV8) and 200 miles of range (EV1). Current draw from 400-volt EVs had a higher thermal impact to cable/connector temperatures when compared to 800-volt EVs, due to a higher current requirement to achieve similar power levels. There was high variance in C rating, a useful metric when comparing the relationship between peak/average charge session power and relative battery pack size, across EVs under test. Ramp rates during initial power transfer were examined, fastest and slowest speeds ranging from 192.5kW/second to 2.6kW/second respectively. A similar analysis of ramp rates was also conducted for OCPP curtailment testing, where EVs underwent a 2-minute 65A curtailment request before returning to full-power charge. Under off-nominal conditions, most EVs experienced variation from the nominal condition profiles. EVs that underwent the full set of NGP defined testing conditions were compared, analysis of which was categorized by 800-volt and 400-volt EVs. It should be noted that the 800-volt EVs had significantly higher peak power ratings than the 400-volt EVs, and thus were more prone to variance. Initial state of charge, battery temperature, and vehicle condition had a considerable impact on peak power levels and charge time for the 800-volt vehicles under test. EVSE limited tests lowered achievable power down to 200kW, greatly impacted 800-volt charging power, and had little to no effect on 400-volt charging power. Adapter testing was performed on a single 400-volt EV, where power and current limitations were found but overall charge time was not significantly impacted. Adapter and boost converter testing was performed on a single 800-volt EV, where both charge power and charge time were greatly impacted. Charge profiles are unique, and comparing such requires analysis that examines starting conditions, vehicle and battery topologies,

Charging↗
DOE OSTI2025

EVs@Scale Next-Gen Profiles - EV Profile Capture 2024

As part of the U.S. DOE EVs@Scale consortium Next-Gen Profiles (NGP) project, the profile capture and analysis of production electric vehicles undergoing high power charging (HPC) is conducted over a wide range of conditions to explore variance and performance. Charge session parameters are collected from both the electric vehicle (EV) and electric vehicle supply equipment (EVSE) at a rate of 10Hz and entered into a time-series database for analysis. These charge profiles are captured under nominal and off-nominal conditions, exploring the impact of battery state of charge (SOC), battery temperature, vehicle condition, smart charge management (SCM), and EVSE limitations. Nominal conditions are defined to be ideal conditions that should transfer the maximum allowable energy in the minimum possible amount of time. Nominal condition profiles are compared across EVs to characterize state-of-the-art EV charging performance against one another. Off-nominal condition profiles are compared against its nominal condition profile counterpart to highlight the variance across less desirable starting conditions within a single EV. This EV Profile Capture 2024 report stands as an update from the EV Profile Capture 2023 report to include the additional EV & EVSE assets tested and analyzed in 2024. The major updates within this report include the addition of three next-generation electric vehicles, added test cases, and further analysis. This expansion of analysis includes power profiles, power distribution, quantifying SOC, energy and range performance, EVSE limitation impacts, boost converter performance, etc. Additionally, NGP time-series data has been used as input towards three national laboratory-led grid modelling efforts: ANL’s IEEE-37 HIL model, INL’s Caldera model, and NREL’s EVI-X model. A summary of these platforms and how NGP has worked to improve their effectiveness has also been added to this years’ report.

Thurston, Sam↗
DOE OSTI2025

EVSE DERMS Controls [SWR-26-010]

An MQTT (Message Queuing Telemetry Transport) and OCPP (Open Charge Point Protocol) based remote smart charging controller framework for AC Electric Vehicle Supply Equipments (EVSEs). The code in this repo allows for the National Laboratory of the Rockies (NLR) controls to interface with the real Distributed Energy Resource Management System (DERMS) and EVSEs in NLR's ESIF Optimization and Control Laboratory (OCL). Different charge management algorithms can be tested to determine which power allocation method is most effective with the overall goal of demonstrating clear and well documented test results as well as providing functional control algorithms which could be utilized to provide effective smart charge management (SCM) at EV charging stations. Different power allocation methods are programmed in lab_demo_controller.py and include allocation based on first come first served, equal sharing, state of charge (SOC), priority factors, and behind the meter control methods.

Panossian, Nadia [National Laboratory of the Rocki↗
DOE OSTI2020

Emerging Technologies for Improved Plug Load Management Systems: Learning Behavior Algorithms and Automatic and Dynamic Load Detection

Plug loads are responsible for a significant portion of the energy consumed in commercial buildings, yet their distributed and ever-changing nature makes them one of the most challenging building end uses to manage. Plug load management systems exist today that utilize smart plugs to meter and control devices at the outlet level, however, their uptake has been relatively slow in part due to the significant labor required for installation and maintenance. Learning behavior algorithms and automatic and dynamic load detection have been identified as two technology areas that could accelerate the adoption of plug load management systems by reducing these labor demands and providing additional energy efficiency and non-energy benefits. Learning behavior algorithms learn occupant behavior and adjust plug load management systems accordingly, allowing for the automatic creation of optimized control schedules. Automatic and dynamic load detection allows a plug load management system to identify devices as they are plugged in to a building and keeps the system up to date as devices are moved throughout a building. In this paper, we present our findings with respect to the current state of these two technologies based on a review of existing research and patents, as well as a series of interviews with companies working in the plug load space. We have found that, as of now, no commercialized solutions exist for these plug load technologies and that more work is needed to bring them to market. In addition, we summarize our findings related to the technology challenges, market barriers, drivers, and opportunities for these technologies moving forward.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

CIO Cloud Strategy

The purpose of the Sandia CIO Cloud Strategy is to establish the strategic direction for the adoption of cloud services and technologies as the prevailing IT solution for Sandia National Laboratories. Sandia’s Chief Information Officer (CIO) will champion unified, site-wide adoption of cloud and will amplify business and mission impacts across the Labs. Sandia’s CIO Cloud Strategy aligns to the Federal Cloud Computing Strategy1 (Cloud Smart) and the Sandia Management and Operating Contract (Prime Contract).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

A framework for integrated dispatching and charging management of an autonomous electric vehicle ride-hailing fleet

The convergence of electrification and automated driving will introduce opportunities to improve the operation and energy-efficiency of transportation systems. This paper discusses the challenges of dispatching autonomous electric vehicles (AEVs) in a ride-hailing fleet and their interactions with charging infrastructure. An integrated decision-making framework for dispatching and charging has been proposed using system optimization approaches. An agent-based platform has been developed for simulating and testing the proposed methods. A case study using New York City taxi data has been performed with different fleet sizes, dispatching strategies, and charging networks. Advantages of optimization-based approaches for AEV fleet management have been studied and demonstrated, for example, for a fleet of 1,750 AEVs to meet 100,000 daily requests, optimization-based centralized fleet management would result in 14% more ride requests satisfied and 43% fewer zero-occupancy miles traveled than if AEVs make independent decisions based on heuristic strategy. Benefits on reducing fleet size and charging downtime from optimization approaches are also comprehensively illustrated.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Implementation and test of an automated control hunting fault correction algorithm in a fault detection and diagnostics tool

Control hunting due to improper proportional–integral–derivative (PID) parameters in the building automation system (BAS) is one of the most common faults identified in commercial buildings. It can cause suboptimal performance and early failure of heating, ventilation, and air conditioning (HVAC) equipment. Commercial fault detection and diagnostics (FDD) software represents one of the fastest growing market segments in smart building technologies in the United States. Implementation of PID retuning procedures as an auto-correction algorithm and integration into FDD software has the potential to mitigate control hunting across a heterogeneous portfolio of buildings with different BAS in a scalable way. This paper presents the development, implementation, and field testing of an automated control hunting fault correction algorithm based on lambda tuning open-loop rules. The algorithm was developed in a commercial FDD software and successfully tested among nine variable air volume boxes in an office building in the United States. The paper shows the feasibility of using FDD tools to automatically correct control hunting faults, discusses scalability considerations, and proposes a path forward for the HVAC industry and academia to further improve this technology.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Implementing multi-settlement decentralized electricity market design for transactive communities with imperfect communication

Recent advances in information and communication technologies and smart metering, provides strategic opportunities for ``prosumers" to reform their conventional energy practices towards more consumer-centric economies. From an operational perspective, managing power distribution networks is becoming more difficult with such active grid-edge systems providing limited to no visibility or control. Transactive Energy (TE) has been emerging as a key enabler towards effectively and efficiently integrating prosumers into competitive electricity markets. This work presents a transactive implementation of community-centric markets. A co-simulation framework is developed for evaluating the proposed market structure with high-fidelity models. Case studies on the IEEE-123 node test system demonstrate that community-centric transactive markets can enable communities of prosumers to operate collaboratively as grid-edge systems. The potential benefits of implementing community-centric TE systems are also illustrated.

Mukherjee, Monish↗
DOE OSTI2025

Active multi-mode data analysis to improve fault diagnosis in AHUs

Faults in heating, ventilation and air conditioning systems can lead to increased energy consumption, occupant comfort issues, and reduced equipment lifetime. Commercial fault detection and diagnosis (FDD) tools has been increasingly deployed in U.S. commercial buildings. While they are helping to achieve energy efficiency and operational reliability, there remain gaps in their fault diagnostic capabilities. The diagnostic results often contain multiple distinct candidate root causes (CRCs) or offer no insight into CRCs. This study developed a novel active rule-based multi-mode data analysis method to enhance diagnostic resolution by applying proven rule sets and additional new rules to data from multiple known operational modes. The proposed method was demonstrated using enhanced air handling unit performance assessment rule sets and validated with the simulated data of two air handling units. New metrics, namely, reduced number of CRCs and improvement ratio, were developed to quantify the improvement of fault diagnostic resolution. The validation results showed that the proposed method effectively reduced the number of CRCs in contrast to analyzing data solely for a single mode of operation. It achieved a median improvement ratio of 80% in 19 test cases.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Transactive Implementation of Decentralized Electricity Market for Grid-Edge Systems

The electricity landscape is evolving towards more decentralized approaches due to the proliferation of distributed energy resources and the participation of increasingly smart consumers and producers (prosumers). Recent advances in information and communication technologies and smart metering, provides strategic opportunities for “prosumers” to reform their conventional energy practices towards more consumer-centric economies. From an operational perspective, managing power distribution networks is becoming more difficult with such active grid-edge systems providing limited to no visibility or control. Transactive Energy (TE) has been emerging as a key enabler towards effectively and efficiently integrating prosumers into competitive electricity markets. This work presents a transactive implementation of community-centric markets. A co-simulation framework is developed for evaluating the proposed market structure with high-fidelity models. Case studies on the IEEE-123 node test system demonstrate that community-centric transactive markets can enable communities of prosumers to operate collaboratively as grid-edge systems. The potential benefits of implementing community-centric TE systems are also illustrated.

transactive, microgrids, centralized market operat↗
DOE OSTI2025

EV Profile Capture 2025: Next-Gen Profiles Project Report

As part of the Next-Gen Profiles (NGP) project, the profile capture and analysis of production electric vehicles undergoing high-power charging (HPC) is conducted over a wide range of conditions to explore variance and performance. Charge session parameters are collected from both the electric vehicle (EV) and electric vehicle supply equipment (EVSE) at a rate of 10Hz and entered into a time-series database for analysis. These charge profiles are captured under nominal and off-nominal conditions, exploring the impact of starting battery state of charge (SOC), battery temperature, vehicle condition, smart charge management (SCM), EVSE limitations and charging adapter usage. Nominal conditions are defined as ideal conditions that should transfer the maximum allowable energy in the minimum possible amount of time. Nominal condition profiles are compared across EVs to characterize state-of-the-art EV charging performance against one another. Off-nominal condition profiles are compared against their nominal condition profile counterparts to highlight the variance across less desirable starting conditions within a single EV.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Building Demand Flexibility: Grid Service Value of Future Market Entrants

The building sector is an important source of demand-side flexibility that is crucial for renewable energy integration in the future power systems. The grid service value of building flexibility, especially that which provides load shifting, has not been analyzed for the United States. We use a technology-agnostic approach based on detailed grid expansion and production cost modeling results to evaluate the capacity, energy, and ancillary service values of a marginal kilowatt-hour (kWh) of daily, shiftable building flexibility as a presumed market entrant in the 2030 U.S. power systems. We find the monthly mean of building flexibility has a range of 0-38 cents/kWh-day, depending on the original usage hour, month, region, building flexibility parameters, and grid scenario. The daily value consists of the highest-value hour each day across all the scenarios has a range of 0-620 cents/kWh-day. The results are provided in an open database for users to obtain the values of specific technologies based on what services can be provided, when, and in what quantities.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Real-time power management technique for microgrid with flexible boundaries

In order to diminish the impacts brought by high penetration of renewable energy on the reliability of distribution systems, some distribution networks (e.g. Chattanooga electric power board system) have deployed smart switches (SSs) to island some areas to mitigate outage losses. However, due to intermittency and sharply changing rate of renewable energy, it is likely to experience insufficient or excessive power for islanded areas. Therefore, a microgrid controller featured with flexible boundaries is proposed. With proposed microgrid controller, the microgrid can not only shrink or expand its boundaries according to current renewable energy supply, but also disconnect/connect to the main grid with a designated SS. Furthermore, to ensure the microgrid controller could obtain suitable boundaries on the time scale of seconds, a real-time power management technique with alternative generating algorithm is designed to generate all possible alternative boundaries and choose the optimal one, which is scalable to any topology. In addition, in order to maintain state of charge of batteries within a desirable range, anti-overcharge/discharge strategies are designed. Four comprehensive experiments verify that the implementation of the microgrid controllers can realise flexible boundaries and deal with sharply changing rate of renewable generation or load on the time scale of seconds.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗