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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Customer-Focused Key Performance Indicators for Electric Vehicle Charging

To systematically improve the public charging experience, EV charging industry stakeholders need to define and measure it precisely. Many stakeholders currently measure aspects of the charging experience, but they typically employ metrics that are either operational in nature, such as charger uptime and mean time between failures, or composite customer satisfaction indices. To improve the customer experience most effectively, the industry needs metrics that define the charging experience from the perspective of the customer, not business operations. Furthermore, industry practitioners need granular metrics to know what specific aspects of the charging experience need improvement. This report defines such customer-focused metrics, called key performance indicators (KPIs).

33 - ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

ChargeX KPIs Transition to SAE

To systematically improve the public charging experience, EV charging industry stakeholders need to define and measure it precisely. Many stakeholders currently measure aspects of the charging experience, but they typically employ metrics that are either operational in nature, such as charger uptime and mean time between failures, or composite customer satisfaction indices. To improve the customer experience most effectively, the industry needs metrics that define the charging experience from the perspective of the customer, not business operations. Furthermore, industry practitioners need granular metrics to know what specific aspects of the charging experience need improvement. This presentation gives an overview of the key performance indicators (KPIs) defined by the ChargeX Consortium.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Understanding EV Charging Pain Points Through Deep Learning Analysis

Current and potential electric vehicle (EV) owners express concerns about the charging infrastructure, mentioning non-functional chargers, prolonged charging times, inconvenient charger locations, long wait times, and high costs as major barriers. Addressing these issues often requires analyzing actual vehicle charging data, which is typically proprietary and inconsistent due to diverse standards and protocols. To understand and improve the EV charging experience, customer reviews are typically used to identify common customer pain points (CPPs). However, there is not a comprehensive method to map customer reviews to a standardized set of CPPs. In collaboration with the National Charging Experience (ChargeX) Consortium, this study bridges these gaps by proposing a Systematic Categorization and Analysis of Large-scale EV-charging Reviews (SCALER) framework. SCALER is an integrated, deep learning framework that segments, actively labels, analyzes, and classifies EV charging customer reviews into six CPP categories. To test its effectiveness, we used SCALER to analyze over 72,000 reviews from customers charging various EV models on different networks across the United States. SCALER achieves a classification accuracy of 92.5%, with an F1 score exceeding 85.7%. By demonstrating real-world applications of SCALER, we enhance the industry’s ability to understand and address CPPs to improve the EV charging experience.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2025

Recommended Practice: Seamless Retry for Electric Vehicle Charging

This work introduces the concept of seamless retry in the electric vehicle (EV) charging domain. Its primary aim is to enhance the reliability and user experience of EV charging by reducing the frequency of user-required interventions in EV charging. This is achieved through an automated retry mechanism that activates upon encountering errors during the EV charging process.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

CHARGE-MAP: An integrated framework to study the multicriteria EV charging infrastructure expansion problem

The widespread adoption of electric vehicles (EVs) in recent years has necessitated the development of effective charging infrastructures. However, charging infrastructure expansion is a multifaceted problem that requires careful consideration of the existing infrastructure, spatiotemporal distribution of charging demands, power-grid capacity, and budget constraints. Here, to approach this complex problem, we present CHARGE-MAP, a data-driven simulation-optimization framework, focused on ensuring meaningful charging experience for individual EV owners. CHARGE-MAP integrates three modules: an agent-based simulation module that estimates spatiotemporal distribution of charging demands by modeling EV adopter mobility and charging behavior; an optimization module that determines optimal new charging station/charger locations and capacities, while minimizing expected detour distances and wait-times with a limited number of new stations; and a power module that determines how to connect the stations to the power grid while maintaining its stability. Using the state of Virginia (consisting of 95 counties and 38 independent cities) as a case study, our results show that CHARGE-MAP can meet the demand of ~198,600 predicted EVs with 1,305 new public charging stations and 2,164 new chargers. It reduces average detour distances for charging by 66% and wait-times at stations by 72% compared to the existing infrastructure. Furthermore, transformer capacity requirement analysis reveals that only 1.8% of residential transformers require upgrades, while over 80% of commercial charging locations can be supported with modest transformer infrastructure (25 to 50 kVA). This indicates that targeted investments can facilitate cost-effective EV integration. Consequently, CHARGE-MAP provides policymakers and urban planners with crucial data-driven insights for effective EV charging infrastructure expansion. Sign up for PNAS alerts.

charging infrastructure↗
DOE OSTI2024

A Review of the Customer Experience at Public Charging Stations and its Effects on Electric Vehicle Purchase and Use

The electric vehicle (EV) market in the U.S. has grown rapidly. However, poor public charging reliability could challenge achieving widespread adoption. We reviewed peer-reviewed articles and market research reports to understand how the public charging experience affects the willingness to purchase EVs among first-time buyers and current owners. Ample literature identifies the measures of the public charging experiences, characterizes the current public charging experience, and identifies the effect of infrastructure availability on EV purchase decisions. However, there is no causal evidence linking the customer experience at public chargers to purchase decisions or intentions for current EV owners or prospective buyers.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Timeouts Best Practices

This study investigates common timeouts encountered in the electric vehicle (EV) charging communications process. Many different timeouts are defined within the EV charging communications protocols. These timeouts can either be a fixed value or a defined range of values. In both cases, the timeout defines the duration of time for which one or both parties in the communications session are expected to wait for some action or process to complete before terminating the charge attempt. These timeout-based terminations are intended to prevent the charging process from becoming stuck indefinitely in any particular step. These terminations also enable a retry of the terminated charging session to begin. However, misaligned timeout values can have a significant negative impact on the user experience. Premature termination of charging sessions due to inappropriate timeout settings can lead to charging failures, causing inconvenience, wasted time, and frustration for users. These disruptions can degrade the overall user experience, making it essential to carefully manage and align timeout values with the relevant actions and processes to ensure reliable and satisfactory EV charging sessions. The core objective of this study is to boost reliability and enhance user experience by conducting a thorough review of timeout-based issues in EV charging and delivering a set of recommendations to modify these existing timeouts. These recommendations are informed by feedback gathered from multiple EV charging partners. This document is intended to inform electric vehicle supply equipment (EVSE) and EV manufacturers, EV charging infrastructure developers, and policymakers responsible for designing and implementing EV charging protocols and systems.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2012

Galactic Cosmic Rays: From Earth to Sources

For nearly 100 years we have known that cosmic rays come from outer space, yet proof of their origin, as well as a comprehensive understanding of their acceleration, remains elusive. Direct detection of high energy (up to 10(exp 15)eV), charged nuclei with experiments such as the balloon-born, antarctic Trans-Iron Galactic Element Recorder (TIGER) have provided insight into these mysteries through measurements of cosmic ray abundances. The abundance of these rare elements with respect to certain intrinsic properties suggests that cosmic rays include a component of massive star ejecta. Supernovae and their remnants (SNe & SNRs), often occurring at the end of a massive star's life or in an environment including massive star material, are one of the most likely candidates for sources accelerating galactic comic ray nuclei up to the requisite high energies. The Fermi Gamma-ray Space Telescope Large Area Detector (Fermi LAT) has improved our understanding of such sources by widening the window of observable energies and thus into potential sources' energetic processes. In combination with multiwavelength observations, we are now better able to constrain particle populations (often hadron-dominated at GeV energies) and environmental conditions, such as the magnetic field strength. The SNR CTB 37A is one such source which could contribute to the observed galactic cosmic rays. By assembling populations of SNRs, we will be able to more definitively define their contribution to the observed galactic cosmic rays, as well as better understand SNRs themselves. Such multimessenger studies will thus illuminate the long-standing cosmic ray mysteries, shedding light on potential sources, acceleration mechanisms, and cosmic ray propagation.

Brandt, Theresa J.↗
DOE OSTI2024

Recommended Actions to Improve Adapter Safety

With the rapid advancement and acceleration in the electric vehicle (EV) industry within the United States, major automakers and EV charging companies are increasingly adopting the North American Charging Standard (NACS) connector style, now officially known as J3400. This shift is expected to enhance charging infrastructure, providing a better customer experience by making it easier for all EV drivers to access a wider network of direct-current (DC) fast chargers (DCFCs). However, the adoption of the J3400 standard presents challenges for many EVs already on the roads and some currently coming off production lines that are equipped with the Combined Charging System (CCS) connector, which this report will refer to as the North American standard, CCS1. These vehicles will need adapters to use new or existing J3400 infrastructure. During this transition, several issues have emerged. Firstly, there is a need to standardize the new connector type to ensure it is interoperable, safe, and reliable. Second, existing CCS EV drivers need a way to access the J3400 network, which will require electric vehicle supply equipment (EVSE) or sites with both connector types, driver-provided adapters to physically convert from CCS to J3400, or EVSE with retained adapters designed for use with the EVSE. Third, adapter standards will need to be written to specify how they will be designed and what evaluations will be needed to ensure safe and reliable performance. To address these challenges, adapters that support different types of charging connectors will be essential. These adapters will play a crucial role in supporting the transition and ensuring continued service for legacy EVs with CCS inlets as the J3400 standard becomes the predominant one in the United States. Consequently, the National Charging Experience (ChargeX) Consortium has investigated and performed a teardown analysis on the different adapter versions on the market. The aim is to create a failure mode and effects analysis (FMEA) on what are expected to be the most common adapter types used in this transition. In order to support this work, we executed an FMEA exercise with the main goal of identifying gaps in the existing adapters' performance and conformance to the most common safety requirements of high-power and high-voltage devices. This effort focused on adapters provided by the driver, as these may present the highest safety and reliability risks. The recommendations made here apply to both retained and driver-provided adapters.

25 ENERGY STORAGE↗
DOE OSTI2026

Evaluations of Connectors, Inlets, and Adapters on Side-Load and Withstand Force

This report examines the mechanical forces that direct current fast charging (DCFC) electric vehicle supply equipment (EVSE) connectors and electric vehicle (EV) inlet ports experience during normal usage. As we witness the fast and growing variety of EVs, charge port locations, and bigger and more powerful EVSE configurations including the SAE J3400 North American Charging Standard (NACS), which has increased the use of adapters with J3400 and J1772 compatibility. These new conditions contribute to increased mechanical forces due to their size, weight and lever arm effect, which creates the need to study and compare these forces to the limits on UL2251 and IEC 62196-1 standards. This report focuses solely on high power DC charging EVSE connectors, EV inlets and adapters for the specific compatibility cases on J3400 with J1772. We first describe the details of the 100N and the 750N side-load evaluations as described in the standards, then present the data obtained by replicating these tests to finally go into more detail on the findings and our recommendations.

100N↗
DOE OSTI2026

Proposal for Direct Detection of Ultralight Dark Matter via Charged Lepton Flavor Violation

We propose a dark matter direct-detection strategy using charged particle decays at accelerator-based experiments. If ultralight ( m ϕ ≪ eV ) dark matter has a misalignment abundance, its local field oscillates in time at a frequency set by its mass. If it also couples to flavor-changing neutral currents, rare exotic decays such as μ → e ϕ ′ and τ → e ( μ ) ϕ ′ inherit this modulation. Focusing on such charged lepton flavor-violating decays, we show that sufficient event samples can enable detection of ultralight dark matter candidates at Mu3e, Belle-II, and FCC-ee.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1979

Characteristics of differential charging of ATS-6

Thirteen days of data collected by the Auroral Particle Experiment onboard the ATS 6 were analyzed emphasizing peculiarities in the electron data attributed to differential charging. On one of these days the satellite was eclipsed by the earth at local midnight. Spectrograms were used to examine the data. It is concluded that differential charging is responsible for returning photoelectrons to the spacecraft up to a couple hundred eV, depending on the spacecraft charge. It is believed that the Minnesota experiment on ATS 6 is largely responsible for producing a potential barrier that returns particles and produces intense spon spots in the count rates.

Johnson, B.↗
NASA NTRS1973

Measurement of the lunar photoelectron layer in the geomagnetic tail

Stable photoelectron fluxes (with energies between 40 and 200 eV) were observed by the Apollo 15 Charged Particle Lunar Environment Experiment (CPLEE). These observations, made in the magnetotail under near vacuum conditions, are compared with numerically calculated photoemission spectra to determine the approximate potential difference between ground and CPLEE'S apertures (a distance of 26 cm). Numerically calculated density and potential distributions are compared with the measured values to provide an estimate of the photoelectron yield function of the dust layer covering the moon.

Reasoner, D. L.↗
DOE OSTI2026

Safe Deep Reinforcement Learning for Active Distribution System Model Predictive Control with EVs and DERs

The temporal and spatial mismatch between PV generation and electric vehicle (EV) charging and discharging may cause voltage violations in active distribution networks. Despite the widespread use of deep reinforcement learning (DRL) in power system optimization and control, it lacks guarantees on constraint satisfaction during both training and deployment. This paper proposes a Lagrangian-based safe DRL approach for model predictive control (MPC) of active distribution systems with large-scale integration of PVs, EVs, and energy storage systems (ESSs). A Transformer-LSTM time-series model is proposed to forecast EV charging demand, which is then formulated as a constraint to ensure charging requirements are met. Using this prediction, a Lagrangian-based safe soft actor-critic (SAC) framework is developed for real-time control in a three-phase unbalanced distribution system, enforcing voltage safety constraints while optimizing the cumulative net reward. By integrating the forecasting model with multi-period constraints, the proposed framework jointly coordinates PV systems, EV charging and discharging, and ESS scheduling within the MPC horizon. Numerical experiments on a modified IEEE 123-bus system with real-world data show that, under a high PV penetration scenario, the proposed method increases the net reward by 30.74% and reduces average voltage violations from 0.0011 p.u. to 0.0002 p.u. compared with standard SAC. Compared with the optimal power flow (OPF) approach, it achieves similar voltage security while yielding lower line losses. It also maintains real-time control capability, reducing operation latency to 53.21 ms per 15-minute control interval. The proposed method remains effective under varying PV/EV penetrations and load conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Improving EV Charger Resiliency for MW Charging Systems

In this paper, the resiliency of a megawatt-scale EV charging station is investigated. Fault detection and tolerance methodologies are presented, based on monitoring the voltage offsets in DABs and switch-level currents in three-phase inverters, with the goal of maintaining operation at rated power during single faults and at reduced power under multiple fault conditions. To validate the effectiveness of the proposed strategy, simulation results at the rated 1 MW power of the charging station are carried out, followed by controller hardware-in-the-loop (CHIL) experiments on a 250 kW section of the station. The results highlight the feasibility of the integrated fault-tolerant methods for resilient, high-power EV charging infrastructure.

Adib, Aswad [ORNL] (ORCID:000000020997056X)↗
NASA NTRS1994

Dynamics Explorer 1, retarding ion mass spectrometer summary spectrograms: 81/280 to 81/365 spin-time spectrograms for H(+), He(+), O(+), N(+), O(++), M/Z=2, and molecular ions

The Retarding Ion Mass Spectrometer (RIMS) experiment onboard the Dynamics Explorer 1 (DE 1) satellite was designed to perform energy and mass-per-charge analysis on low-energy ions (less than 50 eV) with mass/charge ratios ranging from 1 to 40 amu/Z. The DE 1 satellite, carrying the RIMS experiment, was launched into an elliptical polar orbit on August 3, 1981. The approximately 7.5 hour orbit has perigee of 675 km altitude and apogee of 24,875 km altitude. This document, as well as those that follow in this series, contains summary RIMS data spectrograms for each orbit for which RIMS data are available. The RIMS instrument began returning science data on day 280 of 1981 and continued to return usable data until the end of the DE mission in March 1991. It should be noted that studies of the RIMS data set should be conducted only with a thorough awareness of the material described in the introduction section presented here, or in collaboration with a scientist familiar with RIMS data analysis.

Source record↗
NASA NTRS1995

Dynamics Explorer 1, retarding ion mass spectrometer summary spectrograms-82/110 to 82/229 spin-time spectrograms for H(+), He(+), O(+), N(+), O(++), M/Z = 2, and molecular ions

The retarding ion mass spectrometer (RIMS) experiment onboard the Dynamics Explorer 1 (DE 1) satellite was designed to perform energy and mass-per-charge analysis on low-energy ions (less than 50 eV) with mass/charge ratios ranging from 1 to 40 amu/Z. The DE 1 satellite, carrying the RIMS experiment, was launched into an elliptical polar orbit on August 3, 1981. The approximately 7.5 hour orbit has perigee of 675 km altitude and apogee of 24,875 km altitude. this document and those that following in this series, contains summary RIMS data spectrograms for each orbit for which RIMS data are available. The RIMS instrument began returning science data on day 280 of 1981 and continued to return usable data until the end of the DE mission in March 1991. It should be noted that studies of the RIMS data set should be conducted only with a thorough awareness of the material described in the introduction section presented here, or in collaboration with a scientist familiar with RIMS data analysis.

Source record↗
NASA NTRS1995

Dynamics Explorer 1, retarding ion mass spectrometer summary spectrograms-82/230 to 82/265 spin-time spectrograms for H(+), He(+), O(+), N(+), O(++), M/Z = 2, and molecular ions

The retarding ion mass spectrometer (RIMS) experiment onboard the Dynamics Explorer 1 (DE 1) satellite was designed to perform energy and mass-per-charge analysis on low-energy ions (less than 50 eV) with mass/charge ratios ranging from 1 to 40 amu/Z. the DE 1 satellite, carrying the RIMS experiment, was launched into an elliptical polar orbit on August 3, 1981. The approximately 7.5 hour orbit has perigee of 675 km altitude and apogee of 24,875 km altitude. This document and those that follow in this series, contain summary RIMS data spectrograms for each orbit for which RIMS data are available. The RIMS instrument began returning science data on day 280 of 1981 and continued to return usable data until the end of the DE mission in March 1991. It should be noted that studies of the RIMS data set should be conducted only with a thorough awareness of the material described in the introduction section presented here, or in collaboration with a scientist familiar with RIMS data analysis.

Source record↗