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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 217 records · Page 12

Coupled nuclear–electronic decay dynamics of O2 inner valence excited states revealed by attosecond XUV wave-mixing spectroscopy

Multiple Rydberg series converging to the $O_2^+$ c $^{4}Σ^{-}_{u}$ state, accessed by 20-25 eV extreme ultraviolet (XUV) light, serve as important model systems for the competition between nuclear dissociation and electronic autoionization. The dynamics of the lowest member of these series, the $3sσ_g $ state around 21 eV, has been challenging to study owing to its ultra-short lifetime (<10 fs). Here, we apply transient wave-mixing spectroscopy with an attosecond XUV pulse to investigate the decay dynamics of this electronic state. Lifetimes of 5.8 ± 0.5 fs and 4.5 ± 0.7 fs at 95% confidence intervals are obtained for v = 0 and v = 1 vibrational levels of the 3s Rydberg state, respectively. A theoretical treatment of predissociation and electronic autoionization finds that these lifetimes are dominated by electronic autoionization. The strong dependence of the electronic autoionization rate on the internuclear distance because of two ionic decay channels that cross the 3s Rydberg state results in the different lifetimes of the two vibrational levels. The calculated lifetimes are highly sensitive to the location of the 3s potential with respect to the decay channels; by slight adjustment of the location, values of 6.2 and 5.0 fs are obtained computationally for the v = 0 and v = 1 levels, respectively, in good agreement with experiment. Overall, an intriguing picture of the coupled nuclear-electronic dynamics is revealed by attosecond XUV wave-mixing spectroscopy, indicating that the decay dynamics are not a simple competition between isolated autoionization and predissociation processes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Optimal Managed Fast-Charging Model for Electric Vehicle Fleets with High Utilization and Multiple Charge-Acceptance Curves

A predictive control/scheduling optimization model is proposed for managed charging of an electric vehicle (EV) fleet - under time-of-use energy and demand prices, high vehicle utilization frequency (short dwell times), multiple charge- acceptance curves (configurable charging rates), and flexible vehicle demand. This context is particularly relevant for flight schools (small electric aircraft) or other commercial facilities where an EV fleet performs multiple operating and fast-charging sessions on the same day. The proposed model performs both the operational and charging scheduling of the vehicles, which is not typically done for residential managed charging and significantly increases problem complexity. The problem is formulated as a MILP model and a case study of a small fast-charging station is presented. Results demonstrate a significant reduction in operating cost, mainly from peak shaving during high demand price periods, achieved by coordinating the operation of different vehicles, chargers and charging rates.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

A Vehicle-to-Grid planning framework incorporating electric vehicle user equilibrium and distribution network flexibility enhancement

The rapid surge in electric vehicle (EV) adoption, coupled with advancements in charging technologies, emphasizes the critical necessity for expanding EV recharging infrastructure. Simultaneously, the Distribution Network (DN) encounters escalating challenges in meeting charging demand during peak traffic periods. Consequently, there is a mounting demand for the deployment of innovative Vehicle-to-Grid (V2G) technologies to augment the DN’s flexibility in power dispatch and alleviate travel costs for EV users. Hence, this paper proposes an EV-user-equilibrium-(UE)-constrained V2G planning framework that enhances flexibility in the DN. The framework aims to ascertain the optimal placement and capacity of EV charging stations (EVCSs) and V2G charging piles within the Transportation Network (TN). It takes into account the equilibrium condition stemming from competitive EV charging and routing behaviors alongside the optimal expansion of DN energy resources to accommodate the electricity supplied by the V2G piles. This study commences by analyzing EV drivers’ travel decisions, considering the influence of charging and V2G pile locations and sizes. Subsequently, we tackle the Traffic Assignment Problem with User Equilibrium (TAP-UE) model to characterize the steady-state traffic flow distribution of EVs. Following this, we formulate the optimization model for the Coordinated Power and Transportation Network (CPTN), which encompasses the optimal expansion of DN facilities and traffic flow regulation under UE conditions. To mitigate the computational complexity associated with the V2G planning model, we introduce a series of linearization methods to obtain a manageable Mixed-Integer Linear Programming (MILP) solution. Finally, to validate the efficacy of our proposed planning framework, we apply it to two test systems, including a real-world case study. Through these case studies, we explore the necessity and potential benefits of V2G technologies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Universal lower bound on the axion decay constant from free streaming effects

We show that enhancement of the axion relic abundance compared to the standard misalignment contribution generically leads to the production of nonzero momentum axion modes, resulting in warm dark matter behavior and enhanced isocurvature perturbations. It leads to universal constraints on the axion parameter space that are independent of detailed model assumptions and cosmological history. For models enhancing relic abundance with gradient axion modes, observations of the Lyman-$α$ forest impose a lower bound on the axion decay constant, $f_a \gtrsim 10^{15} {\rm GeV}\,(10^{-18}{\rm eV}/m_a)$, from the free-streaming effect. For models relying on the delay of coherent axion oscillations, we obtain a slightly weaker bound, $f_a \gtrsim 10^{14} {\rm GeV}\,(10^{-18}{\rm eV}/m_a)$. We make relatively conservative choices to establish these universal bounds but also provide scaling parameters that can be calibrated for stronger constraints in concrete models and updated as observations improve.

Harigaya, Keisuke [Chicago U., EFI; Tokyo U., IPMU↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗