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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Enabling ITO-free perovskite solar cells through n-doped poly(benzodifurandione) (n-PBDF) electrodes

The burgeoning commercialization of perovskite solar cells (PSCs) is propelled by their exceptional power conversion efficiency (PCE), enhanced stability, and ease of fabrication. However, the rigidity of mainstream transparent conductive oxides (TCOs) limits the adaptability of PSCs to various application scenarios. Although alternative materials such as silver nanowires, carbon nanotubes, graphene, and PEDOT:PSS have been evaluated, the p-type nature confines their device designs to inverted architecture and restricts broader applicability. Here, for the first time, a conducting polymer electrode based on n-doped poly(benzodifurandione) (n-PBDF) was used to replace TCOs in PSCs. The n-PBDF electrodes can be readily fabricated via low-temperature, solution-based processes, indicating substantial potential for reducing manufacturing costs. Furthermore, its integration into PSCs has negligible effect on the phase, morphology, or photophysical properties of the perovskite layer, which is comparable to the typical TCOs. After optimization, the n-PBDF-based device achieves a power conversion efficiency of 12.70 and 11.23%, for rigid and flexible devices, respectively. Our study provides a promising alternative to the widely used TCOs in PSCs, highlighting the advantages of using n-doped conducting polymers in terms of processability and flexibility.

Kumar, Prashant [Purdue University, West Lafayette↗
DOE OSTI2020

Three-Terminal Bipolar Junction Bottom Cell as Simple as PERC: Towards Lean Tandem Cell Processing

Silicon-based tandem cells are a potential upgrade for the dominating PERC technology and can greatly increase the conversion efficiency. A novel approach, the three-terminal (3T) tandem using interdigitated back-contact (IBC) cells, was shown to combine the advantages of four- and two-terminal tandem cells at the expense of a more complex bottom cell fabrication process. In this paper, we propose a simplified and lean PERC-like 3T bipolar junction bottom cell featuring two minority-carrier selective contacts and a single majority-carrier selective contact, and thus resembling a bipolar junction transistor architecture. We present the lean PERC-like fabrication process and explain the working principle by using current-voltage measurements of illuminated 3T IBC cells.

14 SOLAR ENERGY↗
DOE OSTI2024

Development of APCVD BSG and POCl 3 Codiffusion Process for Double-Side TOPCon Solar Cell Precursor Fabrication

This paper presents a commercially viable process for fabricating a high-quality double-side tunnel oxide passivating contact (DS-TOPCon) cell precursor using APCVD-deposited boron silicate glass and ex-situ POCl 3 diffusion in a single high-temperature step, eliminating the need for additional masking and diffusion processes. A two-tier temperature profile was developed, involving a pre-annealing at above 900°C in nitrogen (N2) ambient followed by POCl 3 diffusion at 840°C. We investigated the effect of varying pre-annealing temperatures, ranging from 875°C to 950°C, on the passivation quality and metal-Si contact properties of both n-TOPCon and p-TOPCon layers. The resultant DS-TOPCon cell precursor after silicon nitride (SiNX) passivation exhibited an excellent iV OC of close to 730 mV. In addition, a rapid asymmetric poly-Si thinning technique, developed in this work, enabled adjustment of the front n + poly-Si thickness while maintaining the rear p + poly-Si thickness. Two types of DS-TOPCon cell architectures can be fabricated: i) full-area thin (≈40nm) n-TOPCon layer on the front and ii) selective-area thick (≈200nm) n-TOPCon fingers underneath the metal grid. Device simulations suggest that full-area DS-TOPCon cell with 40 nm n + poly-Si and selective-area DS-TOPCon cell with 200 nm n + poly fingers on the front, fabricated from our current DS-TOPCon cell precursor, can achieve cell efficiencies of 22.1 and 23.5%, respectively. Detailed power loss analysis and device simulation reveal that further improvements in material and device parameters have potential to push the cell efficiencies of DS-TOPCon cell structure beyond 25%, making it a promising alternative to fabricate a high-efficiency next-generation solar cells at low cost.

14 SOLAR ENERGY↗
DOE OSTI2024

Deep Learning-enhanced Block-Diagram Modeling of Solar Power Systems

Data-driven models of power system inverter-based resources are desired to run simulations faster than with detailed electromagnetic transient models, to hide proprietary design details, to support control system design applications, and to aggregate the effects of distributed energy resources. This paper applies a customized Hammerstein Wiener framework to train block diagram models from thousands of electromagnetic transient simulations or experimental test records. The block diagram models integrate with larger grid simulations as voltagecontrolled current sources or current-controlled voltage sources for several simulators. Guidelines for block architecture and training are presented. Three-phase balanced, three-phase unbalanced, and single-phase examples all achieve an acceptable root mean square error of no more than 0.05 per-unit.

Mcdermott, Thomas E. [Private consulting company]↗
DOE OSTI2024

Developments for Novel Module Architecture for Lower CapEx and Improved Recyclability for c-Si PV Modules

Photovoltaic (PV) energy production is currently increasing at a significant rate. A novel module architecture has been demonstrated that has potential for reducing manufacturing cost while improving module reliability and recycling for c-Si PV which utilizes an edge-seal. Referred to as Edge Sealed Module (ESM), this architecture eliminates the vacuum lamination process and cross-linked encapsulants on the interior of the module. Functioning prototypes of c-Si have been fabricated for stress testing in collaboration with National Renewable Energy Laboratories (NREL). These modules are being tested and compared to traditionally manufactured modules. Based on preliminary results, this module architecture is a potentially viable solution for improving the manufacturing cost and recyclability of PV modules while enhancing module performance.

costs↗
DOE OSTI2023

Storing Electricity with Salt and Iron (CRADA Final Report)

As part of the Cyclotron Road program, Inlyte Energy, Inc. investigated low-cost energy storage for the electricity grid via batteries manufactured from abundant materials. Low-cost energy storage can solve the problem of the intermittency of wind and solar power, thereby allowing these sources of energy to scale more rapidly. The project team explored development of a Na-FeCl 2 battery—which had as its starting redox-active materials the abundant materials table salt (NaCl) and iron—that can operate safely and efficiently over a long lifetime, and be produced at very low-cost. The Na-FeCl 2 battery is architecturally similar to the commercialized Zebra (Na-NiCl 2 ) batteries, which have demonstrated safety, efficiency, and long lifetime. This project explored and assessed key design changes, including the use of planar Na-β’’-alumina solid electrolyte and lower temperatures (~200 °C) with polymer seals aimed at decreasing costs.

25 ENERGY STORAGE↗
DOE OSTI2020

Coordinated Multiscale Modeling and Synthesis of Novel Nanostructured Composite Membranes for Solar Fuels Generation (Final Report)

Generation of carbon-based liquid fuels from reduction of CO 2 using solar energy requires the development of improved membranes that provide good ionic conductivity and mechanical properties while minimizing the crossover of gases and reduction products. Using closely coordinated multiscale modeling, synthesis and characterization studies we propose to investigate and develop novel nanostructured composite membranes with applications to conversion of carbon dioxide to storable chemical fuels. The envisioned proton exchange membranes (PEMs) will be based on a support of self- assembling polymer-modified nanoparticles with controllable porosity and tortuosity. The use of a nanoparticle support will provide control of transport and mechanical properties of the membranes. Tuning the copolymer modification of nanoparticles (polymer architectures, brush grafting density) will allow to optimize the ion (proton) transport via the Grotthuss mechanism through narrow hydrated channels while minimizing crossover of gases and reaction products due to the high density of the grafted brush and use of a non-ionic conducting glassy polymers to fill spaces between polymer-grafted nanoparticles. In Phase I synthesis of the copolymers and their attachment to the nanoparticle support will be guided by multiscale simulations that include atomistic molecular dynamics and continuum-level transport simulations. The former will include reactive and non-reactive atomistic simulations that will provide key insight into nanoscale polymer morphology and ion/molecular transport mechanisms need to optimize the copolymer structure. The latter will provide insight into the role of nanoparticle self-assembly on the global transport of ionic and molecular species. The proton conduction and CO 2 /methanol permeability of the membranes will be experimentally characterized as well as simulated and will be correlated with morphology and polymer structure. Prototypes of optimal membranes will be fabricated and characterized at the end of the project. Successful development of improved nanostructured composite PEMs and demonstration of the simulation-guided materials-by-design paradigm will result in our ability to design and synthesize membranes for a wide variety of solar fuels generation and related electrochemical applications.

14 SOLAR ENERGY↗
DOE OSTI2026

Extending Component Lifetime And Improving Inverter Reliability (ECLAIIR)

Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.

14 SOLAR ENERGY↗
DOE OSTI2022

Photovoltaic System Health-State Architecture for Data-Driven Failure Detection

The timely detection of photovoltaic (PV) system failures is important for maintaining optimal performance and lifetime reliability. A main challenge remains the lack of a unified health-state architecture for the uninterrupted monitoring and predictive performance of PV systems. To this end, existing failure detection models are strongly dependent on the availability and quality of site-specific historic data. The scope of this work is to address these fundamental challenges by presenting a health-state architecture for advanced PV system monitoring. The proposed architecture comprises of a machine learning model for PV performance modeling and accurate failure diagnosis. The predictive model is optimally trained on low amounts of on-site data using minimal features and coupled to functional routines for data quality verification, whereas the classifier is trained under an enhanced supervised learning regime. The results demonstrated high accuracies for the implemented predictive model, exhibiting normalized root mean square errors lower than 3.40% even when trained with low data shares. The classification results provided evidence that fault conditions can be detected with a sensitivity of 83.91% for synthetic power-loss events (power reduction of 5%) and of 97.99% for field-emulated failures in the test-bench PV system. Finally, this work provides insights on how to construct an accurate PV system with predictive and classification models for the timely detection of faults and uninterrupted monitoring of PV systems, regardless of historic data availability and quality. Such guidelines and insights on the development of accurate health-state architectures for PV plants can have positive implications in operation and maintenance and monitoring strategies, thus improving the system’s performance.

photovoltaics↗
DOE OSTI2024

Attack Surface of Renewable Energy Technologies

This slide deck presents an overview of the threat landscape for renewable energy technologies. It highlights he growing penetration of renewable resources and potential impact of an attack. Standard architectures are shared to highlight where vulnerabilities may exist and attack paths to reach critical infrastructure. We discuss threat actors and attack paths. Several recent events impacting renewable energy assets or companies are explained.

14 SOLAR ENERGY↗
DOE OSTI2023

AI-based Detection and Defense Against Cyberattacks in Distributed Energy Resources

This study will provide comprehensive artificial intelligence (AI)-based solution tools for network security, malware prevention, and sensor data anomaly detection for distributed energy resource (DER) research, development, and demonstration. DER technologies are energy systems (e.g., solar panels, wind turbines, and energy storage systems) that are often connected to the internet and thus vulnerable to cyberattacks. Cybersecurity should be of primary concern for DERs, which is why we propose an integrated multi-layer cyber-defense system for DERs. This system encompasses risk assessments, network security, malware prevention, and detection of anomalies in the sensor data. Implementation of a comprehensive risk assessment with an overview of the model architecture should be the primary step, and should include the potential impact of experiencing, at a given time, one or more cyberattacks on the system. The second step is to ensure that the network security includes firewalls, intrusion detection, and malware prevention. The third step is to provide solution tools that enable sensor data anomaly detection for DERs. By incorporating these considerations into DER research, development, and demonstration, organizations can help ensure the safety and security of their systems and protect against potential cyberattacks.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

AN UPDATE ON THE STATUS OF A REDUCED FLOW TEST OF A 10MW 700⁰C SCO2 INTEGRALLY GEARED COMPANDER

In order to maintain viability as a future power-generating technology, concentrating solar power (CSP) must reduce its levelized cost of electricity (LCOE). One component of solving this problem is reducing the cost of the power block while simultaneously increasing the efficiency of the thermodynamic cycle. One disruptive technology that has the promise to accomplish this is supercritical CO2 based power cycles. These cycles are conceptually similar to steam cycles; however, they have substantially smaller turbomachinery at equivalent power while also delivering more efficiency at turbine inlet temperatures of 500-700℃. This paper will summarize the current status of a US Department of Energy project to develop machinery to support a 10 MW sCO2 power cycle. The team of Southwest Research Institute® (SwRI®) and Hanwha Power Systems America, proposed to develop an integrally-geared (IG) compressor-expander (compander) for use in a nominal 10 MW-scale CSP supercritical carbon dioxide (sCO2) plant application. This integrally-geared compander (IGC) comprises multiple pinion shafts interconnected on a single bull gear to create a compact package, and utilizes a low-cost, low-speed driver. In addition, the integrally-geared architecture allows each pinion to operate at different rotational speeds to optimize performance and easily allow for inter-stage cooling and turbine re-heat to further enhance both stage and cycle efficiency. The close integration of all turbomachinery elements into a single integrally-geared (IG) machine creates a design that lends itself to power block modularization, which makes it suitable for waste heat recovery, fossil fuel power plants, and especially CSP applications.

14 SOLAR ENERGY↗
DOE OSTI2026

Feasibility of Passive MOSFET Paralleling for Photovoltaic Current-Voltage Curves

Current-voltage curves are an important method of characterizing Photovoltaic (PV) Modules. NREL has developed several low-cost MOSFET-based devices that can characterize PV cells and modules. The size of PV modules that can be measured is limited by the power limitations of a single MOSFET. By using multiple MOSFET devices, each can operate at a lower current, paralleled on resistance is also decreased allowing for higher power loads. Operating multiple devices in parallel can cause an imbalance in current, thus causing some devices to conduct more, pushing them into a thermal runaway condition. To investigate this, an active feedback circuit was tested with paralleled devices using a PV module load. The efficacy, limitations, and design considerations of using parallel MOSFET loads for PV characterization are explored. This work is not new, but the implementation of a paralleled architecture in a low cost characterization tool is novel.

14 SOLAR ENERGY↗
DOE OSTI2020

Nonfused Nonfullerene Acceptors with an A–D–A'–D–A Framework and a Benzothiadiazole Core for High-Performance Organic Solar Cells

Nonfullerene acceptors (NFAs) have contributed significantly to the progress of organic solar cells (OSCs). However, most NFAs feature a large fused-ring backbone, which usually requires a tedious multiple-step synthesis, and are not applicable to commercial applications. An alternative strategy is to develop nonfused NFAs, which possess synthetic simplicity and facile tunability in optoelectronic properties and solid-state microstructures. In this work, we report two nonfused NFAs, BTCIC and BTCIC-4Cl, based on an A-D-A'-D-A architecture, which possess the same electron-deficient benzothiadiazole central core but different electron-withdrawing terminal groups. The optical properties, energy levels, and molecular crystallinities were finely tuned by changing the terminal groups. Moreover, a decent power conversion efficiency of 9.3 and 10.5% has been achieved by BTCIC and BTCIC-4Cl, respectively, by blending them with an appropriate polymer donor. These results demonstrate the potential of A-D-A'-D-A type nonfused NFAs for high-performance OSCs. Further development of nonfused NFAs will be very fruitful by employing appropriate building blocks and via side-chain optimizations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Comprehensive assessment of deep reinforcement learning approaches for economic dispatch in nuclear-driven microgrids

As the electrical grid integrates more variable renewable energy sources such as wind and solar, the demand for distributed and flexible systems to address this increased variability becomes critical. Nuclear-driven microgrids provide a promising solution by offering stable generation to complement intermittent renewables, ensuring grid reliability and operating efficiency. This paper proposes a recurrent deep reinforcement learning framework for optimal economic dispatch in a nuclear-powered microgrid integrating renewable energy sources, small modular reactors, battery storage systems, and balance-of-plant dynamics. A three-agent control architecture is developed, where demand and renewable energy agents act as forecasters, and a reinforcement learning-based dispatch agent performs real-time energy allocation. A nonlinear programming formulation is first used to generate an optimal baseline for benchmarking. The proposed dispatch controller, based on Proximal Policy Optimization enhanced with Long Short-Term Memory networks, exploits temporal correlations in system dynamics by taking advantage of the time series used as inputs to improve policy robustness under uncertainty. Comparative analysis against established deep reinforcement learning methods, including Proximal Policy Optimization with a feedforward architecture, Soft Actor-Critic, and Twin Delayed Deep Deterministic Policy Gradient, demonstrates superior performance. Numerical results indicate that the proposed controller achieves a 0.39% cost reduction relative to the nonlinear programming benchmark and outperforms other learning-based methods by generating additional revenue of up to 0.35%. All reinforcement learning controllers compute dispatch actions in less than 0.3 s, resulting in a computational speedup of more than three orders of magnitude over the nonlinear programming baseline. The findings of this paper highlight their applicability for real-time operation and control in nuclear-integrated microgrids under volatile operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Chemistry of Materials Underpinning Photoelectrochemical Solar Fuel Production

Since its inception, photoelectrochemistry has sought to power the generation of fuels, particularly hydrogen, using energy from sunlight. Efficient and durable photoelectrodes, however, remain elusive. Here we review the current state of the art, focusing our discussion on advances in photoelectrodes made in the past decade. We open by briefly discussing fundamental photoelectrochemical concepts and implications for photoelectrode function. We next review a broad range of semiconductor photoelectrodes broken down by material class (oxides, nitrides, chalcogenides, and mature photovoltaic semiconductors), identifying intrinsic properties and discussing their influence on performance. We then identify innovative in situ and operando techniques to directly probe the photoelectrode|electrolyte interface, enabling direct assessment of structure–property relationships for catalytic surfaces in active reaction environments. We close by considering more complex photoelectrochemical fuel-forming reactions (carbon dioxide and nitrogen reduction, as well as alternative oxidation reactions), where product selectivity imposes additional criteria on electrochemical driving force and photoelectrode architecture. By contextualizing recent literature within a fundamental framework, we seek to provide direction for continued progress toward achieving efficient and stable fuel-forming photoelectrodes.

08 HYDROGEN↗
DOE OSTI2022

Power Electronic Hardware-in-the-Loop (PE-HIL): Testing Individual Controllers in Large-Scale Power Electronics Systems

Large power electronics systems like multi-port autonomous reconfigurable solar power plant (MARS) are increasingly being researched upon to integrate emerging energy sources. MARS connects photovoltaic (PV) systems and energy storage systems (ESSs) to high-voltage direct current (HVdc) links/grids and high-voltage alternating current (ac) transmission grids. As these large power electronics systems incorporate complex hierarchical control systems that are close-by and communicate fast, the control systems require an unique power electronic hardware-in-the-loop (PE-HIL) real-time architecture to evaluate individual controllers. In this paper, a PE-HIL real-time architecture is proposed to evaluate one of the hundreds to thousands of digital signal processors (DSPs) that are a part of the complex hierarchical control system. The DSP connects to a central processing unit (CPU) and a field programmable gate array (FPGA) that form a part of the upper levels of the control system. The DSP is part of the lower level of the control system. The proposed PE-HIL architecture is tested and evaluated. Preliminary test results are presented to showcase the concept.

Debnath, Suman↗
DOE OSTI2022

Aqueous organic flow batteries for sustainable energy storage

Aqueous Organic Redox Flow Batteries (RFBs) have the potential to address the large-scale need for storing electrical energy from intermittent sources like solar- and wind-based generation. Unlike metal-based redox systems, small organic molecules present the prospect of achieving sustainability, by being synthesizable from abundantly available carbon dioxide, water, nitrogen, sulfur, and renewable energy. This mini-review focuses on the progress and challenges in designing water-based RFBs based on small organic molecules that can address the requirements of large-scale energy storage. Further, much of the recent research in this area involves discovering new molecular architectures via computational screening, understanding degradation, and cell configurations to address the techno-economic challenges of extraordinary electrochemical durability, recyclability, low cost of precursor materials, and good solubility. Redox materials for the positive side of the cell are notably few. Future research must continue to focus on these techno-economic challenges through rapid materials discovery.

25 ENERGY STORAGE↗