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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Machine-Learning for Battery Health Diagnosis

Battery health diagnosis often requires time-consuming measurements in laboratory conditions, but these types of measurements are unsuitable for use in real-world application such as electric vehicles. Instead, rapid measurements that can be conducted at varying environmental conditions need to be used to monitor battery health. Machine-learning techniques can then be utilized to connect these rapid measurements to health diagnostic information recorded in the lab. This presentation is a part of a tutorial on using machine-learning methods to analyze and predict battery state.

battery↗
DOE OSTI2021

A Multirange Vehicle Speed Prediction With Application to Model Predictive Control-Based Integrated Power and Thermal Management of Connected Hybrid Electric Vehicles

Abstract Connectivity and automated driving technologies have opened up new research directions in the energy management of vehicles which exploit look-ahead preview and enhance the situational awareness. Despite this advancement, the vehicle speed preview that can be obtained from vehicle-to-vehicle/infrastructure (V2V/I) communications is often limited to a relatively short time-horizon. The vehicular energy systems, specifically those of the electrified vehicles, consist of multiple interacting power and thermal subsystems that respond over different time-scales. Consequently, their optimal energy management can greatly benefit from long-term speed prediction beyond that available through V2V/I communications. Accurately extending the look-ahead preview, on the other hand, is fundamentally challenging due to the dynamic nature of the traffic environment. To address this challenge, we propose a data-driven multirange vehicle speed prediction strategy for arterial corridors with signalized intersections, providing the vehicle speed preview for three different ranges, i.e., short-, medium-, and long-range. The short-range preview is obtained by V2V/I communications. The medium-range preview is realized using a neural network (NN), while the long-range preview is predicted based on a Bayesian network (BN). The predictions are updated in real-time based on the current state of traffic and incorporated into a multihorizon model predictive control (MH-MPC) for integrated power and thermal management (iPTM) of connected vehicles. The results of design and evaluation of the performance of the proposed data-informed MH-MPC for iPTM of connected hybrid electric vehicles (HEVs) using traffic data for real-world city driving are reported.

Automation & Control Systems↗
DOE OSTI2022

Experimental driving performance evaluation of battery-powered medium and heavy duty all-electric vehicles

As the push to reduce air pollutants and carbon dioxide for global warming continues, there is increasing interest in the development and deployment of battery electric vehicles for heavy-duty applications as one of the critical and challenging steps of a broader effort to fully electrify the transportation sector. This study evaluated and quantified the energy consumption rates and energy regeneration rates for three heavy-duty battery electric vehicles (a step van, a yard tractor, and a class 8 truck) using driving cycle tests with a heavy-duty vehicle chassis dynamometer. Information in this area is currently very limited, and yet it is critical to understanding how effective heavy-duty battery electric vehicles will be for a wide range of applications as the electrification of the heavy-duty vehicle market continues to expand. Energy consumption rates ranged from 0.37 and 2.71 kWh/km for most vehicle/cycle combinations. On a diesel liter energy-equivalent basis, the heavy-duty vehicles had approximately 3 to 6 times better energy consumption rates compared to comparable diesel vehicles, with weight normalized energy consumption rates also comparing favorably to the current and future greenhouse gas/fuel economy standards for equivalent diesel vehicles. Based on battery capacities ranging from 80 to 215 kWh, the vehicle ranges varied from 38.18 km to 171.2 km, with ranges of 115.9 km to 171.2 km for most types of driving and lower ranges of 38.18 to 42.20 km for sustained grade cycles. Overall, the advantage of improved energy consumption rates coupled with improvements that can be expected for battery technology and vehicle range, provide promise for expanded use of heavy-duty vehicles going into the future.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Extremum-Seeking-Based Ultra-local Model Predictive Control and Its Application to Electric Motor Speed Regulation

Electric vehicle (EV) market is rapidly expanding. As a critical component of EV, an electric motor needs to accurately follow a reference speed signal while respecting the electrical current constraint for safety. Those requirements are usually formulated as a model predictive control (MPC) problem. However, the performance of traditional model-based MPC depends on the accuracy of the system model, which may not always be guaranteed in reality. Therefore, we utilize a data-driven, model-free predictive control strategy, called ultra-local MPC (ULMPC), to control the speed of an electric motor. To further enhance the control performance of ULMPC, we employ the extremum-seeking control (ESC) to tune the control gain of the ULMPC online. Simulation and hardware experiments demonstrate the enhancement of the extremum-seeking-based ULMPC over a constant-gain ULMPC.

Zhou, Yujing↗
DOE OSTI2023

Fifty years of lithium-ion batteries and what is next?

Here, the first rechargeable lithium batteries were built 50 years ago, at the same time as the Materials Research Society was formed. Great strides have been made since then taking a dream to domination of portable energy storage. During the past two decades, the demand for the storage of electrical energy has mushroomed both for portable applications such as for the iPhone and electric vehicles and for more than 1 GWh grid applications. As storage and power demands have increased, the batteries have evolved with their chemistries being pushed to the limits. This has resulted in the energy densities almost doubling and the cost dropping by more than an order of magnitude. However, the present electrochemical systems are still too costly to penetrate major new markets, still higher performance is required, and environmentally acceptable and sustainable materials are required.

25 ENERGY STORAGE↗
DOE OSTI2025

Quantum sensing for emerging energy technologies

The ability to exploit quantum phenomena has enabled sensing technologies with detection limits below the classical limit. Sensors with applications in energy discovery, production, transportation, and consumption can be enhanced through quantum or hybrid quantum-classical sensors. Here, in this Review, we provide an overview of commercial and emerging quantum sensor platforms and their opportunity areas specific to advanced energy technologies. Key examples include: power grid-enhancing-technologies, where quantum magnetometers can detect powerline and transformer faults; electric vehicle-to-grid applications, where chip-scale atomic clocks can enable grid synchronization; and carbon capture and storage, where quantum gravimeters and single-photo LiDAR can detect microscopic leaks. Quantum sensor deployment requires further research into miniaturization and ruggedization for field deployment, cost-reduction, and workforce development. The maturation of clean energy technologies and quantum sensors provide opportunities for synergy, with the integration of quantum sensors into advanced energy technologies maximizing their security, reliability, and efficiency.

critical metals↗
DOE OSTI2022

A Universal Converter for DC, Single-phase AC, and Multi-phase AC Systems (Final Report)

The goal of this project was to develop a new class of universal power converters that utilize the unique properties of WBG switches, including high reverse voltage blocking and fast switching, to achieve superior performance in universal converters. Some of the features of the proposed converter are as follows: 1) Significantly longer lifetime, higher power density, lower costs associated with bill of materials, and lower shipping, installation, and repair costs compared to the existing WBG-based solutions, 2) Very flexible operation for transferring power between any type of source (dc, single-phase ac, or multi-phase ac) and any type of load (dc, single-phase ac, or multi-phase ac) without using large passive components or additional stages/components, even when the instantaneous values of input and output power are not the same, and 3) High performance operation at all power levels. Considering its advantages and wide range of applications, the proposed converter has the potential to create a new paradigm in power electronics. Among numerous applications of this converter are electric vehicles, wind energy systems, PV systems, industrial motor drives, residential variable frequency drive systems, and nanogrid applications. This project focused on the application of this converter for residential PV, residential motor drive, and industrial motor drive applications, and evaluated the performance of this converter for these applications.

42 ENGINEERING↗
DOE OSTI2025

Evaluating Direct and Indirect Influence on EV Charging Stations Across the US

The adoption of new technology for electric vehicles (EV) and mobility applications can bring underappreciated vulnerabilities to the power grid. One area of potential fraud and adversarial influence is through the business ecosystem of startups that own and deploy EV technology. Yet, there are no models or analyses that map the network of organizations and people that have direct and indirect influence over technologies currently deployed in the grid. To fill this gap, we develop a multilayer network model to measure direct and indirect influence on EV charging stations. First, we create and adversarial socio-technical network (ASTN) model via a data fusion pipeline for different US regions of interest (ROI). Then, we develop an integrated ASTN for Chicago, Los Angeles, New York, and Philadelphia. We rank EV charging companies direct influence within each geographic region as well as indirect influence via social network analysis. While some companies have strong direct and indirect influence (i.e., ChargePoint) others show a mismatch between their influence over charging stations and their position within the social network. For example, Tesla has strong direct influence on stations and weak indirect influence over competitors. In contrast, 7Charge has weak direct influence over stations, but strong indirect influence over competitors.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2025

A Model Predictive Control to Improve Grid Resilience

The following article details a model predictive control (MPC) to improve grid resilience when faced with variable generation resources. This topic is of significant interest to utility power systems where distributed intermittent energy sources will increase significantly and be relied on for electric grid ancillary services. Previous work on MPCs has focused on narrowly targeted control applications such as improving electric vehicle (EV) charging infrastructure or reducing the cost of integrating Energy Storage Systems (ESSs) into the grid. In contrast, this article develops a comprehensive treatment of the construction of an MPC tailored to electric grids and then applies it integration of intermittent energy resources. To accomplish this, the following article includes a description of a reduced order model (ROM) of an electric power grid based on a circuit model, an optimization formulation that describes the MPC, a collocation method for solving linear time-dependent differential algebraic equations (DAEs) that result from the ROM, and an overall strategy for iteratively refining the behavior of the MPC. Next, the algorithm is validated using two separate numerical experiments. First, the algorithm is compared to an existing MPC code and the results are verified by a numerically precise simulation. It is shown that this algorithm produces a control comparable to existing algorithms and the behavior of the control carefully respects the bounds specified. Second, the MPC is applied to a small nine bus system that contains a mix of turbine-spinning-machine-based and intermittent generation in order to demonstrate the algorithm’s utility for resource planning and control of intermittent resources. This study demonstrates how the MPC can be tuned to change the behavior of the control, which can then assist with the integration of intermittent resources into the grid. The emphasis throughout the paper is to provide systematic treatment of the topic and produce a novel nonlinear control compatible design framework applicable to electric grids and the control of variable resources. This differs from the more targeted application-based focus in most presentations.

microgrid↗
DOE OSTI2025

Shape‐Stabilization of Phase Change Materials with Carbon‐Conscious Poly(hydroxy)Urethane Foams

Thermal energy regulation is a significant challenge, contributing to over 30% of annual greenhouse gas emission (GHG) emissions. Phase change materials (PCMs) offer a promising solution by storing thermal energy, which can enable the reuse of waste heat for heating and cooling; however, developing materials for shape-stabilized PCMs remains crucial. This work investigates a self-foaming poly(hydroxy)urethane (PHU), derived from a non-isocyanate polyurethane (NIPU), as a porous support for shape-stabilizing paraffinic and salt hydrate PCMs. PHU-encapsulated paraffinic PCMs exhibited excellent thermal stability over repeated cycles. Thermal stability with salt hydrate PCMs, specifically calcium chloride hexahydrate (CaCl 2 •6H 2 O), is achieved by the incorporation of 5 wt.% barium carbonate (BaCO 3 ) into the PHU foam. This enabled stable cycling for over 48 cycles with desirable thermal properties, i.e., a melting point ≈30 °C, high enthalpy (ca. 138 J g −1 per cycle), and a consistent freezing point ≈20 °C, making it suitable for applications in buildings and electric vehicle battery insulation. Also, incorporating graphite (1.5–10 wt.%) into the foam enhanced the thermal conductivity of shape-stabilized CaCl 2 •6H 2 O during heating and cooling cycles. Overall, the approach detailed here offers a carbon-conscious and chemically tunable material for thermal energy storage.

25 ENERGY STORAGE↗
DOE OSTI2020

Optimization of Oxygen Electrode Combined with Soluble Catalyst to Enhance the Performance of Lithium-Oxygen Battery

Lithium-air batteries possess a high theoretical energy density among the current battery technologies, and are expected to become the driving power for electric vehicles. However, the practical application of lithium air batteries is handicapped by their poor cycle life, high over-charge potential and low energy efficiency. Numerous studies have performed to improve the kinetics of the electrochemical reaction. Making a good oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) catalyst system is under intense scrutiny. In this work, we report a rational design of air electrode, which derives from biomass and presents a three-dimensional porous carbon architecture with high specific surface and large volume expansion coefficient. Combining with a soluble catalyst, this oxygen electrode presents an ultrahigh areal specific capacity of 7.95 mAh/cm 2 and a long cycling life up to 1000 cycles with suppressed polarization (about 0.46 V). Finally, the greatly improved overall performance is due to the synergetic effect of the novel designed oxygen electrode capable of mediating the redox mediators to trigger the ORR and OER process.

25 ENERGY STORAGE↗
DOE OSTI2026

A Reflection on the Evolution of Sodium/Metal Chloride (ZEBRA) Batteries and Lithium-Ion Cathode Materials (1975–2025): A Tribute to Johan Coetzer

This narrative summarizes, in retrospect, the enormous and quietly-conducted contribution of Dr Johan Coetzer, a South African scientist and entrepreneur, to the discovery, development and implementation of the high temperature sodium—metal chloride “ZEBRA” battery (Na/β-Al 2 O 3 , NaAlCl 4 /MCl 2 (M = Ni, Fe)). Here, this research activity was initiated in the mid 1970’s at the Council for Scientific and Industrial Research (CSIR) in South Africa and subsequently developed primarily in partnership with the Atomic Energy Research Establishment (AERE, Harwell, UK), Beta R&D (Derby, UK) and Daimler Benz (Germany), before being transferred to industry worldwide for electric vehicle and energy storage applications, albeit at a relatively low production rate. Coetzer’s wide-ranging, innovative and, at times, unconventional scientific approach also laid the foundation for the discovery and implementation of manganese-based spinel and layered metal oxide cathode materials for the Li-ion battery industry.

ZEBRA Battery↗
DOE OSTI2021

Challenges and needs for system-level electrochemical lithium-ion battery management and diagnostics

The desire for energy-dense and fast-charged battery technology in consumer-electronics, electric-vehicle, grid, and aviation applications is pushing the envelope on materials to cells and pack designs to use and operating conditions. However, some approaches could inherently decrease safety of the battery—thus requiring the development of advanced management and diagnostics. Safety of lithium-ion batteries (LiBs), particularly in multicell configurations, is highly variable and could evolve with use. Existing works primarily focus on cell life and safety diagnostics without considering module and pack-level uncertainties and sometimes imply that cell-level electrochemical diagnostics would work in modules or packs, a naive oversimplification. Using example case studies, we highlight the potential and challenges associated with extending single-cell diagnostics to multiple cells, note the challenge involved, and motivate the research and development and support communities to devote efforts to fill the gap by developing diagnostics at these levels for current- and future-generation LiBs.

25 ENERGY STORAGE↗
DOE OSTI2022

LDRD External Report Summary

In order to lessen societies dependance on fossil fuels to fight climate change, new energy storage and electrification technology is needed. This work highlights the development of non-conventional electrolytes for lithium-ion and redox flow batteries for electric vehicle and grid storage applications.

25 ENERGY STORAGE↗
DOE OSTI2023

Rapid Laser Reactive Sintering of Garnet Li 6.4 La 3 Zr 1.4 Ta 0.6 O 12 and Li 6.1 La 3 Zr 2 Al 0.3 O 12 and Solid-State Electrolytes

All solid-state lithium-ion batteries (ASSLIBs) have gained significant interest in recent years due to their wide range of applications, including mobile devices, electric vehicles, and grid storage. Garnet-type Li 7 La 3 Zr 2 O 12 (LLZO) solid-state electrolyte (SSE) continues to be a significant player in the fabrication of ASSLIBs with excellent ionic conductivity on the order of 10 -3 S•cm -1 with the integration of dopants and sintering aids to assist conductivity and structural evolution during the sintering procedure. Traditional sintering techniques, such as solid-state sintering (SSS), have been proven to yield desirable crystal structures. However, the low ionic conductivity results from inferior microstructure, and lithium loss is a significant challenge. Recently, a relatively novel sintering process called rapid laser sintering (RLS) has shown great potential for achieving fully dense solid oxide electrolytes with less lithium loss, resulting in high ionic conductivity [1] [2] [3] [4]. Here, we applied a modified RLS method using precursor powders instead of pre-synthesized LLZO powders to fabricate Al-doped LLZO (Li 6.1 La 3 Zr 2 Al 0.3 O 12 + 13 wt% Li 2 CO 3 ) and Ta-doped LLZO (Li 6.4 La 3 Zr 1.4 Ta 0.6 O 12 + 5 wt% LiOH•H 2 O) dense electrolytes. This rapid laser reactive sintering (RLRS) could potentially reduce the number of processing steps and therefore lower the manufacturing cost. We hope the rapid high-temperature sintering can densify the electrolyte and avoid the lithium loss for achieving high lithium-ion conductivity. In this work, we optimized a combination of laser parameters (speed and power), initial precursor composition, and controlled atmospheres to discover the optimal conditions for reaching the efficacious crystal structure, microstructure, and ionic conductivity.

25 ENERGY STORAGE↗
DOE OSTI2021

Rapid Laser Reactive Sintering of Garnet Li 6.1 La 3 Zr 2 Al 0.3 O 12 Solid-State Electrolyte

All solid-state lithium-ion batteries (ASSLIBs) have gained significant interest in recent years due to their wide range of applications, including mobile devices, electric vehicles, and grid storage. Garnet-type Li 7 La 3 Zr 2 O 12 (LLZO) solid-state electrolyte (SSE) continues to be a significant player in the fabrication of ASSLIBs with excellent ionic conductivity on the order of 10 -3 S•cm -1 with the integration of dopants and sintering aids to assist conductivity and structural evolution during the sintering procedure. Traditional sintering techniques, such as solid-state sintering (SSS), have been proven to yield desirable crystal structures. However, the low ionic conductivity results from inferior microstructure, and lithium loss is a significant challenge. Recently, a relatively novel sintering process called rapid laser reactive sintering (RLRS) has shown great potential for achieving fully dense solid oxide electrolytes with less surface element loss, resulted in high ionic conductivity. Here, we applied the same RLRS method to fabricate LLZO dense electrolyte with different amounts of aluminum dopants. We hope the rapid high-temperature sintering can densify the electrolyte and avoid the lithium loss for achieving high lithium-ion conductivity. In this work, we optimized multiple combinations of laser parameters (speed, power, and defocus distance), initial precursor composition, and controlled atmospheres to discover the optimal conditions for reaching the efficacious crystal structure, microstructure, and ionic conductivity.

25 ENERGY STORAGE↗
DOE OSTI2024

Flexible Charging to Unify the Grid and Transportation Sectors for EVs at Scale (FUSE)

Electric vehicle (EV) adoption will expand beyond light-duty vehicles charging at lower-power public locations and family residences to include medium- and heavy-duty applications with more powerful charging at public stations and private depots. The Vehicle Grid Integration (VGI) and Smart Charge Management (SCM) pillar in EVs@Scale supports multiple projects. The FUSE project is developing and evaluating enabling technologies, charging solutions, and infrastructure design approaches to investigate the intricacies and potential benefits of vehicle grid integration and the application of smart charge management to charging of electric vehicles at scale. This presentation summarizes the findings of this research in 2024.

ADVANCED PROPULSION SYSTEMS↗