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At least 19 records

A Generic Multivector Model Predictive Control With Symmetric Pulse Pattern for Hybrid Multilevel Converters

This article presents a generic multivector model predictive control (MV-MPC) method that has the potential to be applied to all hybrid multilevel converters (HMCs). It first locates the reference voltage vector in the 120° oblique coordinate to select the three adjacent voltage vectors to be applied over one control cycle. Then, the current tracking is guaranteed through duty cycle optimization and dc capacitor voltages are balanced by evaluating possible switching sequences that belong to the voltage vectors with optimal duty cycles. At last, the optimal switching sequence with a symmetric five- or seven-segment pulse pattern is generated using an external modulator. Here, the proposed generic MV-MPC can significantly improve the quality of the output current, while achieving a constant equivalent switching frequency at the same time. Experimental studies on an all silicon carbide HMC prototype, i.e., an active neutral point clamped converter with cascaded H-bridge, are presented to validate the effectiveness of the proposed MV-MPC strategy.

42 ENGINEERING↗

A Phase-Disposition PWM Enabled Model Predictive Control for a Nine-Level Inner-Interleaved Hybrid Multilevel Converter

This article brings forward a phase-disposition pulse width modulation enabled model predictive control (PDPWM-MPC) for a nine-level inner-interleaved hybrid multilevel converter (9L-IHMC). Firstly, three layers of virtual space vector diagrams (VSVD) are established based on the sign patterns of the original and virtual reference vectors in the abcframe, respectively, to achieve the phase-disposition pulse width modulation (PD-PWM) in a non-independent three-phase way. Then, three adjacent virtual vectors in the third-layer VSVD, together with their optimized duty cycles, are applied to guarantee the optimal current tracking. Finally, through the use of the duty cycle alternation approach, dc-link and floating capacitors voltages are balanced and circulating currents are mitigated as well. The proposed PDPWM-MPC can not only enable the decoupling of the low-and high-frequency stages in the 9L-IHMC, but also reduce both output current ripples and computational burden. In addition, it can achieve a constant equivalent switching frequency and address the disproportion of power losses associated with the PD-PWM. Here, both simulations and experiments on a silicon carbide device-based prototype substantiate the effectiveness of the proposed control strategy.

42 ENGINEERING↗

Ion-chain sympathetic cooling and gate dynamics

Sympathetic cooling is a technique often employed to mitigate motional heating in trapped-ion quantum computers. However, choosing system parameters such as number of coolants and cooling duty cycle for optimal gate performance requires evaluating trade-offs between motional errors and other slower errors such as qubit dephasing. The optimal parameters depend on cooling power, heating rate, and ion spacing in a particular system. In this study, we aim to analyze best practices for sympathetic cooling of long chains of trapped ions using analytical and computational methods. We use a case study to show that optimal cooling performance is achieved when coolants are placed at the center of the chain and provide a perturbative upper bound on the cooling limit of a mode given a particular set of cooling parameters. In addition, using computational tools, we analyze the trade-off between the number of coolant ions in a chain and the center-of-mass mode heating rate. We also show that cooling as often as possible when running a circuit is optimal when the qubit coherence time is otherwise long. These results provide a roadmap for how to choose sympathetic cooling parameters to maximize circuit performance in trapped-ion quantum computers using long chains of ions.

Cooling & trapping↗

A Generic Two-Vector Model Predictive Control for Hybrid Multilevel Converters

In this article, a generic two-vector model predictive control (TV-MPC) strategy is proposed for the hybrid multilevel converters (HMCs). The proposed method selects two optimal voltage vectors among all the vector candidates using a geometric positioning approach to reduce the computational burden, which is a common issue in the existing MPC methods for HMC. Then duty cycles of the two selected vectors are optimized to minimize the current tracking error, such that the current tracking performance can be enhanced compared with the conventional MPC. In addition, the voltages of the floating dc capacitors can be balanced by evaluating all switching sequences that belong to the optimal voltage vectors with optimal duty cycles. Here, the concept of the proposed TV-MPC is generic and applicable for any HMCs. A typical HMC based on active neutral-point-clamped topology is adopted as a case study in this work. Comprehensive simulation and experimental studies are performed on an all silicon-carbide HMC prototype to validate the effectiveness of the proposed control scheme.

42 ENGINEERING↗

FORCE Update 2024

The Framework for Optimization of Resources and Economics (FORCE) tool suite is the U.S. Department of Energy’s Nuclear Integrated Energy Systems (IES) Program flagship tool suite for technoeconomic IES analysis of IES. This tool suite is useful for analysis designed to evaluate and improve the technoeconomics of energy production systems, particularly for systems including nuclear technology. In this report, we document the development activity for the FORCE tool suite to extend its capabilities as performed during fiscal year 2024. In addition to reliability and accessibility, capability is one of the three standards guiding the development of the FORCE tool suite and the software codes that are its constituent parts. Extending the capabilities of the FORCE tool suite allows analysis both within the IES program as well as industry, university, and laboratory partners to perform analysis with more accuracy, insight, and impactful narrative. Four areas of capability development were the focus of activity this year: economic parameter uncertainty quantification, multiresolution analysis, components-to-optimization workflow automation, and statespace construction workflows for real-time optimal control. In economic parameter uncertainty quantification, the ability of HERON to capture risk due to scenarios (weather and energy demand uncertainty) was expanded to also include uncertainties in financial parameters such as capital cost or operation and maintenance costs. By including these sources of uncertainty, which are sometimes very large compared with scenario uncertainty, HERON is better able to capture the risk posed by investment in various IES technology. Because of this, analysts can also consider the reduction in risks that can be realized by choice of some technologies. In multiresolution analysis, development activity extended on work completed previously. In fiscal year 2023, methods for decomposing time series signals, such as demand, solar and wind availability, and price profiles, were analyzed and down-selected to those most effective at splitting signals into different resolutions. These resolutions allow considering the influence of different energy demand and supply behaviors across different time scales. For example, energy demand might be divided into seasonal, weekly, and hourly profiles. In fiscal year 2024, this preliminary work was extended and implemented within the Risk Analysis Virtual Environment (RAVEN) risk and uncertainty analysis platform, which is used throughout the FORCE framework. This development of the “multi-resolution time series analysis” (MR-TSA) module in RAVEN allows training synthetic history generators on complex time series. These synthetic history generators can then be used in HERON for generating scenarios that represent possible market and weather scenarios that can be analyzed on different time scales. We envision completing this work in the future, implementing multiresolution dispatch optimization strategies that can make the most beneficial use of these stratified time histories. In components-to-optimization workflow development, workflows for translating user inputs of components into algorithms for algebraic optimization were selected and implemented. Similar algorithms within the Holistic Energy Resource Optimization Network (HERON) were separated from the main code base of HERON and gathered with the components-to-optimization workflows in the new Dispatch Optimization Variable Engine (DOVE) software library. This modularization allows FORCE users to analyze dispatch optimization and energy system duty cycles independently of HERON, which previously was a burdensome task. Additionally, these dispatch optimization algorithms, set up in an independent library, can now be used across all software applications within FORCE, especially including the real-time optimal control software Optimization of Real-time Capacity Allocation (ORCA). Allowing FORCE software to share dispatch optimization algorithms within a single library allows for improved software maintenance and reliability. In statespace characterization workflow development, alternative workflows for optimizing dispatch with additional technical accuracy was the focus, particularly to improve the real-time optimization decision making in ORCA. Using algorithms and workflows initially developed for the Feasible Actuator Range Modifier (FARM), workflows for determining the statespace representation of IES were identified and demonstrated. The resulting dispatch optimization required a more robust optimization algorithm than that originally used in HERON (and moved to DOVE), which required adding an alternate workflow to DOVE that can more accurately match the behavior of physical systems using a partial differential equation representation. In conclusion, capability developments in the FORCE tool suite in fiscal year 2024 have improved the ability of the FORCE tool suite to perform

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Magnetic cores with high reluctance differences in flux paths

Embodiment of the present invention includes a magnetic structure and a magnetic structure used in a direct current (DC) to DC energy converter. The magnetic structure has an E-core and a plate, with the plate positioned in contact or in near contact with the post surfaces of the E-core. The E-core has a base, a no-winding leg, a transformer leg, and an inductor leg. The no-winding leg, the transformer leg, and the inductor leg are perpendicular and magnetically in contact with the base. The plate is a flat slab with lateral dimensions generally larger than its thickness. The plate has a plate nose that overlaps a top no-winding leg surface of the no-winding leg with a no-winding gap area to form a no-winding gap with a no-winding gap reluctance. The plate also has a plate end that overlaps a top inductor leg surface of the inductor leg with an inductor gap area to form an inductor gap with an inductor gap reluctance. In some embodiments, e.g., where the duty cycle is less than 50 percent, the inductor gap reluctance will be designed to be less than the no-winding gap reluctance. In these cases, the majority of the magnetic flux that passes through the transformer leg will return through the inductor leg, instead of through the no-winding leg. The inductor and no-winding gap reluctances can he adjusted, so that the electromotive force applied to a charge passing through the inductor will partially cancel the electromotive force applied by the transformer secondary. The gap reluctance ratio can be defined, so that the difference in secondary and inductor electromotive forces is equal to the output voltage defined by an optimal no-ripple duty cycle. In this way no changing current is required through the inductor to create a dI/dt inductive voltage drop across the output inductor. Zero output current ripple is achieved.Various embodiments of the plate, plate shape, and no-winding leg are disclosed. These embodiments allow achieving a high ratio of no-winding gap reluctance to inductor gap reluctance, for practical, affordable magnetic material structures and aspect ratios. A high gap reluctance ratio enables zero output current ripple for the high transformer turns ratios that are needed to achieve high input to output voltage ratios. The embodiments therefore allow achieving low output current ripple for 48 V or higher input voltages, 1 V or lower output voltages, and high output currents.

Yao, Yuan↗

Exceptional points in a passive strip waveguide

Abstract Exceptional points (EPs) in non‐Hermitian systems have attracted significant interest due to their unique behaviors, including novel wave propagation and radiation. While EPs have been explored in various photonic systems, their integration into standard photonic platforms can expand their applicability to broader technological domains. In this work, we propose and experimentally demonstrate EPs in an integrated photonic strip waveguide configuration, exhibiting unique deep wave penetration and uniform‐intensity radiation profiles. By introducing the second‐order grating on one side of the waveguide, forward and backward propagating modes are coupled both directly through second‐order coupling and indirectly through first‐order coupling via a radiative intermediate mode. To describe the EP behavior in a strip configuration, we introduce modified coupled‐mode equations that account for both transverse and longitudinal components. These coupled‐mode formulas reveal the formation of EPs in bandgap closure, achieved by numerically optimizing the grating’s duty cycle to manipulate the first‐ and second‐order couplings simultaneously. Experimental observations, consistent with simulations, confirm the EP behavior, with symmetric transmission spectra and constant radiation profiles at the EP wavelength, in contrast to conventional exponential decay observed at detuned wavelengths. These results demonstrate the realization of EPs in a widely applicable strip waveguide configuration, paving the way for advanced EP applications in nonlinear and ultrafast photonics, as well as advanced sensing technologies.

Materials Science↗

VALVE TIMING AND AREA PROFILE SELECTION FOR HYDRAULIC PUMPS AND MOTORS

A significant amount of research has been conducted to select valve timing and area profiles that create efficient and quiet hydraulic pumps and motors. Numerous active valve architectures have been modeled and optimized, but the rationale for the final solution is often unclear. The solution is usually highly dependent on the modeled valve geometry constraints and the duty cycle of the pump or motor for which the valve was optimized. This paper first presents an ideal valve area profile and what compromises are made to design for a working pressure and speed range. The ideal valve area profile is resolved using a piston-cylinder model with a pressure dependent bulk modulus and variable valve openings. The piston-cylinder is first modeled as a pump with passive (check) valves and a specified pressure drop across the valves to determine the ideal area profile. A generalization of the ideal timing is demonstrated for positive pressure differential and positive rotation direction, also known as the first quadrant. The effects on valve timing when varying pressure are shown for the first quadrant, but the trends can be extrapolated for all quadrants of operation. The ideal valve profile is implemented as fixed valve timing, meaning the valve area is only a function of the timing angle, in a pump-motor. The pump-motor is simulated in one rotation direction through a pressure range. Performance is high in pumping operation, but when the pressure differential is reversed, cylinder pressure spikes ensue. Two strategies to modify an ideal valve area profile are presented: timing grooves and a pressure shifted valve timing. Timing grooves reduce pressure spikes and cavitation in the cylinder but generally increase throttling losses. A pressure shifted valve timing has lower throttling energy losses, making it the favored solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Utilizing data-based modeling with low life cycle GHG emissions algae biofuels for engine optimization

Aquatic microalgae are a highly promising feedstock for the production of biocrude and tailored biofuels, with distinct advantages over traditional terrestrial crops, such as reduced land use and avoidance of food production competition. However, unlocking their full potential requires the development of biofuels with low life cycle greenhouse emissions biofuels, such as algae biofuels, which can significantly reduce the environmental impact of the transportation systems without requiring a complete overhaul of existing engine technology. In this study, we employ cutting-edge data-based AI modeling techniques to optimize the performance of heavy-duty engines, with a focus on transitioning towards biofuels with low life cycle greenhouse emissions biofuels. Our methodology offers significant advantages over traditional sweep testing, enabling efficient and accurate optimization of engine performance with minimal time and resources consumption. Our findings demonstrate the potential of utilizing this approach, with up to 55% NOx emissions reductions and up to 2% reduction in fuel consumption compared to the baseline optimized point. Moving forward, we plan to utilize a 30% blend of algae biofuels with diesel fuel, with the ultimate goal of achieving up to 60% lifecycle GHG emissions. Lastly, we plan to compare the results with 100% renewable biodiesel to add an additional dimension of investigating the impact of fuel chemistry on engine optimization. Overall, this study underscores the vital importance of biofuels for reducing the carbon footprint of the transportation sector and supporting a sustainable future. By harnessing the power of data-based AI modeling with low life cycle greenhouse emissions biofuels, we can accelerate the adoption of more environmentally friendly transportation systems and reduce their impact on the planet. Our findings contribute to this transition and offer insights for developing efficient and effective strategies for addressing global climate change.

09 BIOMASS FUELS↗

Hybrid Electric Drivetrain Testing and Design (CRADA CRD-17-00699 Final Report)

Hybrid electric drivetrains have recently become of a great interest in the medium and heavy-duty vehicle market as it enables substantial reduction of petroleum use, vehicle level fuel use and criteria pollutant emissions. These vehicle performance improvements are not only economically beneficial for business operations relying on large fleets of vehicles, they are also paramount for curbing energy use and the negative effects of vehicle operations on the environment. Efficient Drivetrains, Inc. (EDI), acquired in July 2018 by Cummins, Inc., is a small company focused on development of medium and heavy-duty hybrid electric drivetrains. EDI has already developed the general hardware architecture of their drivetrain system, but there is still a significant effort to be done on optimizing the system in terms of control strategies and component sizing in order to maximize the benefits of the hybrid drivetrain. Historically, hybrid electric systems have proven to deliver better fuel economy in certain applications than their conventional counterparts. Particular areas of advantageous applications are vocations with kinetically intensive transient duty cycles and operations requiring some sort of power take off whose power demand is not well matched to the size of the vehicle’s main engine, thus forcing it to operate in extremely inefficient operating modes. Hybrid drivetrains can be at a disadvantage when pressed into duty cycle operation consisting of extensive steady state highway cruise due to various design compromises optimized for more transient operation. This can also lead to increase in vehicle emissions if the system is not optimized properly. Ample opportunity for extensive optimization is needed to overcome these obstacles.

33 ADVANCED PROPULSION SYSTEMS↗

Hybrid Electric Drivetrain Testing and Design (CRADA CRD-17-00699 Final Report)

Hybrid electric drivetrains have recently become of a great interest in the medium and heavy-duty vehicle market as it enables substantial reduction of petroleum use, vehicle level fuel use and criteria pollutant emissions. These vehicle performance improvements are not only economically beneficial for business operations relying on large fleets of vehicles, they are also paramount for curbing energy use and the negative effects of vehicle operations on the environment. Efficient Drivetrains, Inc. (EDI), acquired in July 2018 by Cummins, Inc., is a small company focused on development of medium and heavy-duty hybrid electric drivetrains. EDI has already developed the general hardware architecture of their drivetrain system, but there is still a significant effort to be done on optimizing the system in terms of control strategies and component sizing in order to maximize the benefits of the hybrid drivetrain. Historically, hybrid electric systems have proven to deliver better fuel economy in certain applications than their conventional counterparts. Particular areas of advantageous applications are vocations with kinetically intensive transient duty cycles and operations requiring some sort of power take off whose power demand is not well matched to the size of the vehicle’s main engine, thus forcing it to operate in extremely inefficient operating modes. Hybrid drivetrains can be at a disadvantage when pressed into duty cycle operation consisting of extensive steady state highway cruise due to various design compromises optimized for more transient operation. This can also lead to increase in vehicle emissions if the system is not optimized properly. Ample opportunity for extensive optimization is needed to overcome these obstacles.

33 ADVANCED PROPULSION SYSTEMS↗

Enabling off-highway diesel engine downsizing and performance improvement using electrically assisted turbocharging

Internal combustion engine (ICE) downsizing through various turbocharging configurations is generally known by the powertrain design community as an effective means to reduce frictional losses, increase waste heat recovery, and improve fuel efficiency while increasing engine power density. However, often is the case that turbocharging strategies, including variable geometry turbochargers, and regulated two-stage turbochargers, incur the performance tradeoff between transient response and fuel economy (pumping losses) at high engine speeds. For off-highway vehicles having particularly transient and high-powered duty cycles, efforts to improve this tradeoff and increase operational flexibility have turned to evaluating various electrified air system architectures. In this study, a 4.5 L diesel-ICE configured with an electrically driven compressor (eBooster ® ) is placed in series with a conventional turbocharger and integrated into a 48 V mild-hybrid powertrain architecture. The objective of this powertrain configuration is to enable engine downsizing by 34%, replacing the current 6.8 L ICE platform with the hybridized 4.5 L ICE concept. The commercial 1-D simulation software GT-SUITE is used for powertrain system development and system optimization. Development and validation of the GT-SUITE model and air system controls is concurrently supported through experimental data collection. The simulation model development includes using machine learning methods for optimizing exhaust gas dilution, injection timing, and eBooster ® power to improve steady-state and transient brake-specific fuel consumption while minimizing criteria pollutant emissions. It was found that total specific fluid consumption over the standardized non-road transient engine duty cycle could be reduced by 18% over the current 6.8 L engine by using an optimized eBoosted 4.5 L engine. The hybridized 4.5 L engine concept concurrently showed sufficient transient response capability and nearly an order of magnitude reduction in duty cycle total soot production.

25 ENERGY STORAGE↗

Medium-duty Urban Range Extended Connected Powertrain (Final Scientific Technical Report)

The project goal is to develop and demonstrate a Class 4 delivery vehicle that reduces fuel consumption by 50% or more when compared to an equivalent vehicle with a conventional internal combustion engine (ICE) powertrain driven on a comparable duty cycle. This shall be achieved using a plug-in hybrid electric (PHEV) configuration that optimizes the efficiency of the ICE. The proposed solution aims to be commercially viable for fleets to procure the system without additional incentives. To accomplish this, commercialized light-duty vehicle electric drive components shall be used. A PHEV configuration for Class 4 MD/HD vocational vehicles using commercialized light-duty vehicle electric drive components will require the development of a new topology for the hybrid powertrain applicable to medium-duty vehicles. Prior research indicates that an identified novel power split device could provide a fuel economy improvement potential of up to 51%. Utilizing an advanced battery management algorithm that will allow operation closer to the battery limits, the project will minimize battery capacity to achieve the desired performance results while maintaining the cost target for high commercialization. The solution must be cost effective to purchase and integrate into a Class 4 delivery vehicle, deliver the intended fuel economy improvement, and perform as well, or better, than the current conventional ICE powertrain. MD/HD vehicles typically require higher power, which is one factor that results in high costs for the powerful electric machines typical in larger HEV/PHEV systems. A key element in the project is the use of commercially available Bosch electric drive components (e.g., motors, batteries, and power electronics) from the larger passenger vehicle segment. This leverages the economies of scale to decrease cost and improve reliability beyond current purpose-built low-volume commercial vehicle components. These innovations will result in a game-changing PHEV system that meets DOE’s 50% fuel consumption reduction target, grants the vehicle an uncompromised driving range, and achieves the cost point for widespread adoption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Depot Charging Schedule Optimization for Medium- and Heavy-Duty Battery-Electric Trucks

Charge management, which lowers charging costs for fleets and prevents straining the electrical grid, is critical to the successful deployment of medium- and heavy-duty battery-electric trucks (MHD BETs). This study introduces an energy demand and cost management framework that optimizes depot charging for MHD BETs by combining an energy consumption machine learning model and a linear program optimization model. The framework considers key factors impacting real-world MHD BET operations, including vehicle and charger configurations, duty cycles, use cases, geographic and climate conditions, operation schedules, and utilities’ time-of-use (TOU) rates and demand charges. The framework was applied to a hypothetical fleet of 100 MHD BETs in California under three different utilities for 365 days, with results compared to unmanaged charging. The optimized charging solution avoided more than 90% of on-peak charging, reduced fleet charging peak load by 64–75%, and lowered fleet energy variable costs by 54–64%. This study concluded that the proposed charge management framework significantly reduces energy costs and peak loads for MHD BET fleets while making recommendations for fleet electrification infrastructure planning and the design of utility TOU rates and demand charges.

Song, Shuhan↗

Potential Adoption and Benefits of Co-Optimized Multimode Engines and Fuels for U.S. Light-Duty Vehicles

Exploring a diverse portfolio of technologies for decarbonization is crucial to understanding the potential impacts of different technological solutions and their associated environmental implications. Using high-octane, high-sensitivity biofuel blends in co-optimized multimode engines can increase engine efficiency and reduce vehicle emissions. Here, the multimode engine research focuses on the benefits of light-duty vehicle engines, which can operate in multiple modes depending on the vehicle's load. Low-temperature combustion can improve efficiency and reduce emissions (such as those from oxides of nitrogen and particulate matter) during low-load operation, while spark ignition performance is maintained in high-load operation. These advanced engines can be optimized to run on blends of biobased fuels. This analysis models scenarios for potential market adoption of co-optimized multimode vehicles fueled by three different bioblendstocks: ethanol, isopropanol, and isobutanol. An integrated modeling approach is used to forecast the energy and environmental impacts of the deployment of co-optimized multimode vehicles and fuels in the light-duty sector over the 2020-to-2050 time horizon. The multidisciplinary approach combines vehicle sales modeling, system dynamics modeling of the biorefining industry, and life cycle assessment to estimate the emissions and energy benefits. The models consider market forces such as consumer preferences for vehicle attributes, biofuel supply and demand dynamics subject to biorefinery capacity build-out and bioresource constraints, and forecasted changes to the U.S. bulk energy system over time. Market adoption of co-optimized vehicles is evaluated across a wide parameter space for incremental vehicle cost and engine efficiency improvement. This analysis reveals that the deployment of co-optimized multimode fuels and vehicles results in up to a 5% reduction in annual sector-wide life cycle greenhouse gas (GHG) emissions by 2050, relative to a business-as-usual scenario, but is also indicates environmental trade-offs, such as higher life cycle water-use. Emission benefits could potentially increase beyond 2050, as the new technologies penetrate the market and gain a foothold. Results also show that, under certain circumstances, vehicles with engines co-optimized for use with high-octane, high-sensitivity biofuel blends can be cost-competitive with conventional gasoline, while reducing GHG emissions. Our modeling results indicate that co-optimized multimode fuels and engines can be strategically leveraged in tandem with electrification to decarbonize the light-duty sector. Co-optimized vehicles could play a role in the early years of the time horizon, while electric vehicles (EVs) could become more competitive in the later years, highlighting the complementary benefits of these technologies for GHG reductions.

Oke, Doris↗

System-of-systems optimization of hydrogen infrastructure for heavy-duty freight corridors: The interstate 10 case study

Medium and heavy-duty freight transportation requires hydrogen energy infrastructure that is cost-effective, operationally reliable, spatially coherent, and resilient to demand variability along major corridors. This paper presents an integrated hydrogen corridor planning framework using Oak Ridge National Laboratory's OR-AGENT that couples freight-driven, route-resolved hydrogen demand modeling with optimized station siting, sizing, and station-level techno-economic analysis. The framework is demonstrated for the Interstate 10 freight corridor and the Houston-to-Los-Angeles region. Hydrogen demand is derived from high-resolution origin–destination freight data, duty-cycle characterization, and physics-based energy consumption modeling. Candidate refueling sites are selected from existing heavy-duty diesel fueling locations and optimized subject to onboard storage and station capacity constraints. Resulting station throughputs are evaluated using established techno-economic models for electrolytic hydrogen production and dispensing infrastructure. Results show that a regional, portfolio-level aggregation, average dispensed electrolytic hydrogen cost of $6.87–$7.26/kg is currently feasible, and is strongly influenced by demand density and utilization.

Sujan, Vivek [ORNL] (ORCID:0000000269882342)↗

Silver Nanowire-Indium Zinc Oxide Composite Flexible Transparent Conducting Electrodes Made by Spin- coating and Photonic Curing

Realizing high-throughput, low-cost perovskite solar cell (PSC) manufacturing is highly sought-after in photovoltaic (PV) research in recent years. To fully achieve roll-to-roll (R2R) manufacturing of PSCs, it is important to consider the flexible transparent electrode (TE). PET/ITO is a commonly used substrate for making flexible PSCs. When optimizing transparent conducting materials, there is a tradeoff between sheet resistance (Rsh) and optical transparency. Because commercial PET/ITO substrates are made with slow (~1 m/min) vacuum deposition processes, they tend to be expensive. Therefore, it would be advantageous to develop a high-throughput, R2R compatible, solution-deposition approach for fabricating the TE on PET substrates. While various solution-deposition processes, such as blade coating or slot-die coating, can achieve the desired web speed of > 10 m/min, there is still a need to improve the post-deposition annealing step. One promising post-deposition processing technique is intense-pulsed-light processing, also known as photonic curing. Photonic curing delivers short (0.01 – 100 ms) pulses of broadband (200 – 1500 nm) light from a xenon flash lamp to the samples. Any materials in the sample stack that absorb light will convert the impinging light pulse into heat within the sample, which drives changes in the sample (calcination, phase change, crystallization, etc.). Photonic curing has three main advantages over thermal annealing: 1. Faster processing speed (milliseconds or seconds). 2. Compatibility with plastic substrates. 3. Smaller physical footprint and less wasted energy. Since the light pulses are on for a short time, the intensity can be high while the total energy delivered to the sample is low, minimizing damages to the plastic substrates. In this work, a hybrid TE material is fabricated on PET substrates using photonic curing. The hybrid TE material contains a layer of silver nanowires (AgNWs) and a layer of metal-oxide (InOx, ITO, IZO, etc.). The AgNWs increase the light absorbed by the film during the photonic curing process, which leads to higher processing temperatures, possibly improving the conversion of the metal-oxide layer. The AgNWs also enhance the electrical conductivity of the final TE layer after photonic curing. A AgNW and metal-oxide bilayer is formed by spin coating each solution onto the PET substrate sequentially followed by a single photonic curing process. We use average optical transmittance (Tavg) from 400 to 700 nm and average Rsh to evaluate the TE performance. The following photonic curing parameters are varied to optimize Tavg (maximize) and Rsh (minimize): Pulse voltage, pulse envelope, number of micro-pulses, duty cycle, number of pulses, and pulse repetition rate. Preliminarily, we also observe a significant impact on the TE properties by the volume of AgNW deposited during the spin coating deposition step. Using dispense volumes of 80 µL and 20 µL, we achieve samples with Tavg = 73%, Rsh = 19 Ω/sq, and roughness = 9 nm, and Tavg = 83%, Rsh = 58 Ω/sq, and roughness = 5.6 nm, respectively, after photonic curing.

14 SOLAR ENERGY↗

Low-Cost, Easy-To-Integrate and Reliable Grid Energy Storage System with 2 nd Life Lithium Batteries

Batteries retired from electric vehicles have the potential to extend their service as low-cost stationary energy storage systems. However, disperse battery state of health (SOH) and nonuniform battery characters often lead to compromised battery performance and reliability, which greatly hinder their adoption. A Heterogenous Unifying Battery (HUB) system is proposed to stage 2 nd life battery bricks for a period, and enable them to attain improved SOH uniformity, performance, and reliability before being sold for 2 nd life applications, while simultaneously providing grid services. It may offer a technically and economically advantageous solution for the broad utilization of 2 nd use batteries. The goal of this project was to develop the hardware and software that enables the key functions of the HUB system. The first achievement of the project was the development of a 1kW scale proof-of-concept (POC) system, which comprises (i) a modular plug-n-play DC-DC power converter matrix with isolated series output connections to achieve fully independent control of energy flow to each of the connected battery units at low voltage; (ii) enhanced model based control that drives each batteries’ SOH towards uniformity while collectively providing grid energy storage services; and (iii) comprehensive procedures to perform battery diagnostics and prognostics. The second achievement was the development of a 100kW scale HUB system and demonstrated its performance of re-establishing battery SOH uniformity through a period of battery cycling operation. The final HUB system incorporates six DC-DC power converter matrices paired with six battery bricks. Hot swapping of a single battery brick while maintaining consistent system power was demonstrated and system operation was validated to be capable of implementing the approved grid duty cycle and of balancing and conditioning the battery bricks. Through the course of the project, the team optimized the building-block design, form-factors, and adjusted life balancing control. An up-sized 250kW Scale was developed and deployed in October 2022 with pack-level battery form factors, see photo in Figure 1 The third achievement of the project was to perform a techno-economic analysis in order to better understand the cost and revenue potentials in this new “recondition-then-resell" value proposition. The final TEA quantified the economics of new Li-ion batteries as well as second-life batteries processed via reconditioning and traditional binning. Results showed the reconditioned second-life batteries in this project to be economically favorable and viable in grid energy storage markets. The TEA results were published in the Applied Energy journal. The fourth achievement of the project was to deliver a tech-to-market plan for the HUB system that includes funding, IP, and manufacturing strategies. The final T2M plan outlines a business strategy in which the HUB provides a B2B service to EV companies as an alternative to battery recycling that can prepare batteries for 2nd life applications. A company named Smartville Inc. was founded to carry on the commercialization, funding, and technical IP licensing activities of the OPEN project.

25 ENERGY STORAGE↗