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NASA NTRS2004

Assessment and Control of Spacecraft Charging Risks on the International Space Station

The International Space Station (ISS) operates in the F2 region of Earth's ionosphere, orbiting at altitudes ranging from 350 to 450 km at an inclination of 51.6 degrees. The relatively dense, cool F2 ionospheric plasma suppresses surface charging processes much of the time, and the flux of relativistic electrons is low enough to preclude deep dielectric charging processes. The most important spacecraft charging processes in the ISS orbital environment are: 1) ISS electrical power system interactions with the F2 plasma, 2) magnetic induction processes resulting from flight through the geomagnetic field and, 3) charging processes that result from interaction with auroral electrons at high latitude. Recently, the continuing review and evaluation of putative ISS charging hazards required by the ISS Program Office revealed that ISS charging could produce an electrical shock hazard to the ISS crew during extravehicular activity (EVA). ISS charging risks are being evaluated in an ongoing measurement and analysis campaign. The results of ISS charging measurements are combined with a recently developed model of ISS charging (the Plasma Interaction Model) and an exhaustive analysis of historical ionospheric variability data (ISS Ionospheric Specification) to evaluate ISS charging risks using Probabilistic Risk Assessment (PRA) methods. The PRA combines estimates of the frequency of occurrence and severity of the charging hazards with estimates of the reliability of various hazard controls systems, as required by NASA s safety and risk management programs, to enable design and selection of a hazard control approach that minimizes overall programmatic and personnel risk. The PRA provides a quantitative methodology for incorporating the results of the ISS charging measurement and analysis campaigns into the necessary hazard reports, EVA procedures, and ISS flight rules required for operating ISS in a safe and productive manner.

Koontz, Steve↗

NASA NTRS2014

Spacecraft Charging Analysis of a CubeSat

Spacecraft charging occurs when charged particles from the surrounding space plasma environment contact a spacecraft and unequal charging currents result in a net charge density accumulation on or in spacecraft materials. Charging becomes a threat when differential potentials between two points on the spacecraft or between the spacecraft and the ambient space environment build to the level that electric fields associated with the potentials exceed the electric breakdown strength of the spacecraft materials and electrostatic discharge arcs are generated. Electrostatic discharges resulting from spacecraft charging can adversely affect telemetry and cause irreparable damage to electronics. Other spacecraft charging effects include damage of solar arrays and thermal protection, enhancement of contamination of surfaces, and degradation of optics. Typically, the large government and commercial space programs include spacecraft charging analysis as part of the design process. CubeSat projects, however, usually do not have the time or funding to include a spacecraft charging analysis due to their low budget and quick-turnaround requirements. CubeSat projects also tend to rely heavily on commercial "off-the-shelf" products, many of which are not qualified for use in space, and are particularly vulnerable to the effects of the space environment. As the demand for longer and more complex CubeSat missions increases, it is becoming more and more important to consider the effects of spacecraft charging in the design process. Results of surface charging analysis using Nascap-2k on a typical CubeSat design for a polar orbit scenario are illustrated. These results show that for a polar orbiting CubeSat, spacecraft charging could be an issue and steps should be taken to mitigate the effects for these small satellites.

Willis, Emily M.↗

DOE OSTI2014

Study of Nonlinear Dynamics of Intense Charged Particle Beams in the Paul Trap Simulator Experiment

The Paul Trap Simulator Experiment (PTSX) is a compact laboratory device that simulates the nonlinear dynamics of intense charged particle beams propagating over a large distance in an alternating-gradient magnetic transport system. The radial quadrupole electric eld forces on the charged particles in the Paul Trap are analogous to the radial forces on the charged particles in the quadrupole magnetic transport system. The amplitude of oscillating voltage applied to the cylindrical electrodes in PTSX is equivalent to the quadrupole magnetic eld gradient in accelerators. The temporal periodicity in PTSX corresponds to the spatial periodicity in magnetic transport system. This thesis focuses on investigations of envelope instabilities and collective mode excitations, properties of high-intensity beams with significant space-charge effects, random noise-induced beam degradation and a laser-induced-fluorescence diagnostic. To better understand the nonlinear dynamics of the charged particle beams, it is critical to understand the collective processes of the charged particles. Charged particle beams support a variety of collective modes, among which the quadrupole mode and the dipole mode are of the greatest interest. We used quadrupole and dipole perturbations to excite the quadrupole and dipole mode respectively and study the effects of those collective modes on the charge bunch. The experimental and particle-in-cell (PIC) simulation results both show that when the frequency and the spatial structure of the external perturbation are matched with the corresponding collective mode, that mode will be excited to a large amplitude and resonates strongly with the external perturbation, usually causing expansion of the charge bunch and loss of particles. Machine imperfections are inevitable for accelerator systems, and we use random noise to simulate the effects of machine imperfection on the charged particle beams. The random noise can be Fourier decomposed into various frequency components and experimental results show that when the random noise has a large frequency component that matches a certain collective mode, the mode will also be excited and cause heating of the charge bunch. It is also noted that by rearranging the order of the random noise, the adverse effects of the random noise may be eliminated. As a non-destructive diagnostic method, a laser-induced- fluorescence (LIF) diagnostic is developed to study the transverse dynamics of the charged particle beams. The accompanying barium ion source and dye laser system are developed and tested.

LIF↗

DOE OSTI2015

What is the Impact of Utility Demand Charges on a DCFC host

The PEV Electric Vehicle Supply Equipment (EVSE) delivered by The EV Project included both AC Level 2 and DCFC units. Over 100 of these dual-port Blink DC fast chargers were deployed by The EV Project. These DCFCs were installed in workplaces and in publicly accessible locations near traffic hubs, retail centers, parking lots, restaurants, and similar locations. The Blink DCFC is capable of charging at power up to 60 kW. Its dual-port design sequences the charge from one port to the other, delivering power to only one of two vehicles connected at a time. The actual power delivered through a port is determined by the PEV’s on-board battery management system (BMS). Both the power and the total energy used to recharge a PEV can represent a significant cost for the charging site host. Many electric utilities impose fees for power demand as part of their commercial rate structure. The demand charge incurred by a customer is related to the peak power used during a monthly billing cycle. This is in contrast to the cumulative total energy usage that is the more familiar utility charge seen for most residential services. A demand charge is typically assessed for the highest average power over any 15 minute interval during the monthly billing cycle. One objective of The EV Project was to identify and elucidate the motivations and barriers to potential DCFC site hosts. The application of electric utility demand charges is one such potential barrier. This subject was introduced in the paper: DC Fast Charge - Demand Charge Reduction1. It discussed demand charge impact in general terms in order to focus on potential mitigation actions. This paper identifies specific cases in order to quantify the impact of demand charges on EV Project DCFC hosts.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2015

What is the Impact of Utility Demand Charges on an Alternating Current Level 2 Elective Vehicle Supply Equipment Host?

The EVSE delivered by The EV Project included both residential and non-residential units. Approximately 4,000 non-residential ACL2 EVSE were installed in workplace environments, fleet applications and publicly accessible locations near retail centers, parking lots, and similar locations. The Blink ACL2 utilized in The EV Project is capable of charging at up to 7.2 kW power, although most vehicles in The EV Project charged at about 3.7 kW. This power can be a significant additional electrical load for the charging site host. This concern becomes greater when several EVSE are in operation on a site at the same time. Many electric utilities include maximum power demand as part of their commercial rate structure. The demand charge incurred by a commercial customer is related to the peak power used during a monthly billing cycle. This is in contrast to the total energy usage that is the more familiar utility charge seen for residential service. A demand charge is typically calculated based on the highest average power level over a 15 minute period during the monthly billing cycle and is not a cumulative-type charge. One objective of The EV Project was to elucidate the motivations and hindrances to potential site hosts of non-residential EVSE. The imposition of electric utility demand charges represents such a potential hindrance. This subject was introduced in the paper: DC Fast Charge - Demand Charge Reduction1, specifically dealing with DC Fast Chargers. The concept remains relevant to ACL2 EVSE, especially when several EVSE are deployed at the same site, which occurred frequently in the EV Project. In fact, the average number of ACL2 EVSE per site was 2.58 and varied by market from 1.79 to 3.45 per site. This paper identifies the impact of demand charges on non-residential ACL2 hosts in The EV Project

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Use and Siting of Electric Vehicle Charging Stations in Juneau, Alaska

This report details a study of electric vehicle (EV) Level 2 charging stations in Juneau, Alaska. Utilization analyses of six public over five years and 250 residential chargers over two years are included, and a composite score is introduced to identify optimal locations for future charging stations that target residents of manufactured and multifamily housing (MMFH) in Juneau. We find that public charging station usage is very location-dependent, with three chargers in use more than 60% of days during the peak hour of the day (which ranges from 10 a.m. to 7 p.m.), including a charger near residential housing, illuminating potential needs for additional public chargers in those areas. Residential charging utilization typically occurs overnight - opposite to most public charging stations analyzed - and spikes after 10 p.m. This suggests that Alaska Electric Light & Power Company's time-of-use charging program, which lowers electricity rates at 10 p.m. to incentivize overnight charging, is very effective. Residential charging data also show that households tend to charge 15 hours per week, or 9% of the time, meaning that multiple households could likely share one charger if one were provided near MMFH locations. This is supported by residential charging session analysis, which shows that the median household has around two night charging sessions per week. The EV siting analysis identifies areas of high housing density, low access to public chargers, and unconstrained feeders. A cluster of MMFH parcels in Douglas demonstrated the highest composite scores considering all factors, being the only area to have a perfect score of 2.25.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

Technology solutions to mitigate electricity cost for electric vehicle DC fast charging

Widespread adoption of alternative fuel vehicles is being hindered by high vehicle costs and refueling or range limitations. For plug-in electric vehicles, direct-current fast charging (DCFC) is proposed as a solution to support long-distance travel and relieve range anxiety. However, DCFC has also been shown to be potentially more expensive compared to residential or workplace charging. In particular, electricity demand charges can significantly impact electricity cost for fast charging applications. Here we explore technological solutions that can help reduce the electricity cost for electric vehicle fast charging. In particular, we consider thousands of electricity rates available in the United States and real-world vehicle charging load scenarios to assess opportunities to reduce the cost of DCFC by deploying solar photovoltaics (PV) panels and energy storage (battery), and implementing a co-location configuration where a DCFC station is connected to an existing meter within a commercial building. Results show that while the median electricity cost across more than 7000 commercial retail rates remains less than $0.20/kWh for all charging load scenarios considered, cost varies greatly, and some locations do experience significantly higher electricity cost. Co-location is almost always economically viable to mitigate fixed cost and demand charges, but the relative benefit of co-locating diminishes as station size and utilization increase. Energy storage alone can help mitigate demand charges and is more effective at reducing costs for 'peaky' or low-utilization loads. On the other hand, PV systems primarily help mitigate energy charges, and are more effective for loads that are more correlated with solar production, even in areas with lower solar resource. PV and energy storage can deploy synergistically to provide cost reductions for DCFC, leveraging their ability to mitigate demand and energy charges.

25 ENERGY STORAGE↗

DOE OSTI2021

Data mining of plug-in electric vehicles charging behavior using supply-side data

This paper aims to better understand the charging patterns of plug-in electric vehicles (PEVs) and identify factors that may significantly impact PEVs’ charging behavior. We collected 189,864 supply-side charging session data over 13 months from 821 charging stations in Illinois from ChargePoint. Through descriptive and regression analyses, we characterize the distributions of key charging behavior indicators, including charging location, dwell time, and battery start state of charge (SOC), and quantify the impacts of closely related factors on these charging behaviors. In this work, we find that: (1) PEVs are more likely to charge in the morning at multifamily commercial locations with a lower start SOC compared with single family residential locations; (2) Weekday and morning sessions are more likely to utilize workplace charging and have shorter dwell time compared with weekend and afternoon sessions; (3) Single family residential area and locations with Levels 1/2 chargers have a higher start SOC and longer dwell time compared with other locations and DC fast chargers (DCFCs). These findings provide policy insights to identify potential time and locations to incentivize PEVs for grid services, as well as identify critical location categories for further charging infrastructure investment to better reduce range anxiety and promote PEV adoption.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Understanding particle size effect on fast-charging behavior of graphite anode using ultra-thin-layer electrodes

Extreme fast charging (≤ 15 min) of lithium-ion batteries is highly desirable to accelerate mass-market adoption of electric vehicles. However, significant capacity fading, as well as safety issues due to the lithium plating caused by the fast charging rate, limit its implementation. In this study, we investigated the fast-charging capability of graphite materials with various particle sizes. To eliminate the Li + ion concentration gradient effect across the thickness of the electrode, ultra-thin-layer graphite electrodes were developed to investigate the "real" fast-charging capability of graphite at the particle level. Electrochemical assessments as well as microscopic characterizations revealed that smaller particles exhibited superior fast-charging performance, featuring enhanced capacity reversibility, faster charging rate, and less lithium plating under the same fast-charging conditions. It is shown that small-particle graphite (mean radius of 3.3 μm) could withstand a 4C charge (to 80 % state-of-charge) without plating, with minimal plating occurring at 6C. Thicker particles exhibited plating at lower C-rates. Since the experimental data could not directly explain whether intra-particle diffusion limitations or interfacial reaction limitations dominated the plating mechanism, the pseudo-2-dimensional model was used to evaluate the most likely plating mechanism. The model suggested that particle-level diffusion is the dominant mechanism contributing to plating at high rates. Finally, this work provides comprehensive insights into the particle size effects on fast-charging capability, offering a better understanding of fast-charging behavior and valuable guidance for designing optimal electrode architecture for high-rate lithium-ion batteries.

25 ENERGY STORAGE↗

DOE OSTI2022

Electrifying New York City Ride-Hailing fleets: An examination of the need for public fast charging

This report assesses the scale of public fast charging needed to electrify approximately 20,000 vehicles across the yellow cab and for-hire segments in New York City. The analysis considers real-world trip data in conjunction with driver home locations, overnight charging access rates, driver schedules, and more. Outcomes indicate that the existing charging network in New York City is not adequate even in the most optimistic scenario; 1,054 150-kW ports are required when 15% of drivers have access to overnight charging, whereas 367 150-kW ports are needed when 100% of drivers have access. Results also indicate that although charging is demanded in areas nearby high trip demand, fast charging ports are also demanded in areas near driver residences as a supplement for home charging in scenarios with limited overnight charging access. These findings motivate investment into both overnight charging and public fast charging to meet the charging demands of ride-hailing fleets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Techno-economic and environmental assessment of solar-based electrical vehicles charging stations integrated with hydrogen production

With the growing interest in adopting both commercial and residential electric vehicles (EVs) utilizing green renewable energy, the techno-economic assessment of EV charging stations with solar energy is a critical aspect of the transition to sustainable transportation. However, battery storage capacity for variable solar energy production is becoming increasingly less cost-effective due to the higher Lithium price. Complementing solar energy production and battery storage, the potential of combining green hydrogen fuel energy integration with solar energy for EV charging stations can provide a sustainable alternative to reach carbon-free transportation. This research aims to assess the technical and economic viability of grid-connected Photovoltaic (PV)-based EV charging stations across Kentucky based on existing EV load profiles. The study considers different PV system sizes, locations, orientations, and the impact of net metering policies and electricity tariffs on the profitability of the PV systems. Further, using excess PV energy, Lithium-Ion batteries will be charged, which will be assessed considering the techno-economic and environmental impacts of green EV charging stations. To further enhance green renewable energy production, we also investigated the feasibility of on-grid PV-based EV charging stations with Lithium-Ion batteries charged during low tariff periods. In comparison with these scenarios, we studied the potential benefits of integrating green hydrogen energy production using excess solar energy with and without governmental incentives. Furthermore, we assess the environmental impact of these green EV charging stations across Kentucky. Our study indicates that EV stations across Kentucky have similar techno-economic feasibility with insignificant deviation in the levelized cost of electricity, payback period, and PV energy fraction with/without Li-Ion batteries. However, with green hydrogen production, the marginal H 2 production cost indicates the first scenario (on-grid PV energy charging) is the most significant, roughly 10 USD/Kg, which is similar to 1.5 times higher than the second (charging during low-tariff periods) and the third scenarios (power grid to satisfy the demand deficit - approximately 6.5 USD/Kg). From the environmental perspective, the second scenario would be a good option for either hybrid EV/H 2 or EV charging stations in Kentucky, factoring in the hydrogen production cost and the carbon footprint.

08 HYDROGEN↗

DOE OSTI2019

Modeling charging behavior of battery electric vehicle drivers: A cumulative prospect theory based approach

The behavior of drivers in charging a battery electric vehicle (BEV) can be influenced by psychological factors such as personality and risk preference. This paper proposes a cumulative prospect theory (CPT) based modeling framework to describe the charging behavior of BEV drivers. CPT captures an individual’s attitude and preference toward risk in the decision-making process. A BEV mass-market scenario is constructed using the 2017 National Household Travel Survey (NHTS) data. This work applies the CPT-based charging behavior model to study the battery state-of-charge (SOC) when drivers decide to charge their vehicles, charging timing and location choices, and charging power demand profile under the mass-market scenario. In addition, sensitivity analyses are used to examine the drivers’ risk attitudes and public charger network coverage. BEV drivers who reflect a higher degree of risk-seeking tend to charge vehicles at a lower SOC. Some home charging shifts to workplace and public charging as the public charger network expands, but home charging still plays the most significant role in BEV use. The power demand from public chargers increases critically with BEV expansion and has a larger impact on the power grid. The time-of-use (TOU) electricity rate can shift peak power demand to off-peak periods from midnight to early morning.

2017 National Household Travel Survey (NHTS)↗

DOE OSTI2023

Learning-based demand-supply-coupled charging station location problem for electric vehicle demand management

We present a learning-based, demand-supply-coupled optimization model for the charging station location problem (CSLP), aiming to integrate the concept of electric vehicle (EV) charging demand management into the planning of charging infrastructures. In stage one, a gradient boosting-based learning model is developed to predict the charging demand of a charging station based on 15 defined features. Next, in stage two, a demand–supply-coupled CSLP model is developed to optimize the total charging usage rates of both existing and newly selected charging stations. We design a gradient-based stochastic spatial search algorithm to solve the proposed model. A case study with 6-year charging event data from Kansas City Missouri is performed. Results show that the proposed method can generate satisfactory charging demand predictions, and can increase charging usage rates by 14%, outperforming two benchmark approaches. Furthermore, the results of this research are poised to guide agencies in identifying optimal locations for new charging stations.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2014

Particle size effects in particle-particle triboelectric charging studied with an integrated fluidized bed and electrostatic separator system

Fundamental studies of triboelectric charging of granular materials via particle-particle contact are challenging to control and interpret because of foreign material surfaces that are difficult to avoid during contacting and measurement. The measurement of particle charge itself can also induce charging, altering results. Here, we introduce a completely integrated fluidized bed and electrostatic separator system that charges particles solely by interparticle interactions and characterizes their charge on line. Particles are contacted in a free-surface fluidized bed (no reactor walls) with a well-controlled fountain-like flow to regulate particle-particle contact. The charged particles in the fountain are transferred by a pulsed jet of air to the top of a vertically-oriented electrostatic separator consisting of two electrodes at oppositely biased high voltage. The free-falling particles migrate towards the electrodes of opposite charge and are collected by an array of cups where their charge and size can be determined. We carried out experiments on a bidisperse size mixture of soda lime glass particles with systematically varying ratios of concentration. Results show that larger particles fall close to the negative electrode and smaller particles fall close to the positive electrode, consistent with theory and prior experiments that larger particles charge positively and smaller particles charge negatively. The segregation of particles by charge for one of the size components is strongest when its collisions are mostly with particles of the other size component; thus, small particles segregate most strongly to the negative sample when their concentration in the mixture is small (and analogous results occur for the large particles). Furthermore, we find additional size segregation due to granular flow, whereby the fountain becomes enriched in larger particles as the smaller particles are preferentially expelled from the fountain.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

Interpretable Machine Learning for Characterizing Electric Vehicle Charging Behavior: Insights from Real-World Data

As electric vehicle (EV) adoption rises globally, concerns about the impact on aging electrical grids grow, particularly regarding the charging behavior of EV drivers. This study analyzes real-world driving and charging data from Ford battery electric vehicles (BEVs) collected between 2018 and 2019 to develop interpretable models that characterize charging behavior and quantify influencing factors. Prior research has relied on assumptions regarding driver behavior, often overlooking actual charging patterns. By employing generalized linear mixed models (GLMMs), this work offers insights into how various elements, such as next trip distance and state of charge (SOC), influence charging decisions. The dataset comprises over three million park-trip pairs from 1,997 vehicles, revealing that features related to driving behavior significantly dictate charging behavior, while infrastructure and regional factors have lesser impacts. The findings suggest that existing simulation models may oversimplify EV charging behavior assumptions. This work utilizes real-world EV driving and charging data to train interpretable models that describe charging behavior and quantify the factors most associated with how drivers use charging infrastructure. This research underscores the need for interpretable, data-driven methodologies to inform future EV infrastructure planning and grid management.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

DOE OSTI2018

Distribution System V1G PEV Charging Impacts Report

Plug-in Electric Vehicles (PEVs) are a clean form of transportation that will require a new control paradigm between the energy supply and the transportation sectors as PEV adoption continues to grow. Meeting the projected PEV energy demands will require implementing PEV / grid integration methods beyond charging PEVs at incentivized times. MATLAB was used to simulate the daily vehicle trip demands and charging processes of hundreds of PEVs. The National Household Travel Survey (NHTS) data was used as the source of vehicle travel data, and EV Project provided measured PEV charging data to calibrate the PEV population models. GridLAB-D analyses used a prototypical feeder and PEV population models to evaluate residential PEV V1G (unidirectional) charging in multiple locations, V1G PEV distribution feeder impacts, Time-Of-Use (TOU) rate effects on PEV charging, and charging control methods. The key findings were: optimized charging using California TOU rates could result in savings of over $20 per month for non-TOU program participants; non-TOU participants consume 30-40% of PEV charging energy; about 50% of PEV drivers have their transportation fuel bill reduced by 50 dollars per month; 6.6kW charging will cause distribution transformer power limits to be exceeded on prototypical feeders if the household PEV adoption rate reaches 0.75 PEVs per home; distribution feeder loading is proportional to PEV charging rate; a method was devised to remotely identify when residential transformer overloads occur using changes in PEV charging rate; and the low-cost TOU period is composed of a distribution of PEVs needing small, medium and larger energy needs that can be more effectively shifted as groups to mitigate the TOU peak power and enable a higher PEV adoption rate per household without overloading distribution transformers.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Inventory of Public Key Cryptography in US Electric Vehicle Charging

Electric vehicles (EVs) and charging infrastructure are networked systems, which employ high-level communications in support of charging and grid service decisions. Public key cryptography (PKC) underlies much of the security and privacy protections of the information exchange. We are entering a new epoch where quantum computing threats must be seriously considered. A sufficiently large quantum computer, so named Cryptographically Relevant Quantum Computer (QRQC), will be able to perform the mathematical operations to efficiently attack the underpinnings of traditional PKC, thus jeopardizing the digital foundations for trust, communications security, and data security. Estimates suggest a QRQC can break public key encryption and digital signatures in the manner of tens to hundreds of hours, compared to traditional computing that would demand more than 10 18 years in a brute force-style attack. A consensus belief of quantum theorists, quantum experimenters, and cryptographers suggest that the quantum threat will be likely realized in the next twenty years. To address the threat, post-quantum cryptography, which is cryptosystems that are designed to be secure against both traditional and quantum computing threats, must be adopted. Migration from traditional PKC to quantum-resilient cryptography is a global undertaking and likely represents the largest transition in computing history. The nascent state of EV public key infrastructure, combined with limited adoption of the vehicle secure charging features, presents an opportunity to establish a preference for quantum-resistant cryptography as a step on the migration path. Delays will stunt the efforts as rapidly accelerating EVs sales and huge infrastructure investments will create large growing bases of long-lived vehicles and infrastructure. Migration preparations can commence while NIST continues the process to standardize post-quantum cryptography (PQC), which are quantum-resilient algorithms designed to be secure against traditional and quantum computing threats. The first step in preparing EV charging is to identify the presence of traditional public key cryptography algorithms and applications. With this objective in mind, this report is intended to advise the vehicle manufacturers, charging station manufacturers, charging station operators, charge network providers and other EV charging stakeholders with information on traditional PKC application and the potential risks when PKC becomes insecure. This report, the first in a series of reports discussing the topics existing at the confluence of post-quantum cryptography adoption and EV charging, identifies traditional public key applications employed and identifies potential consequences of leaving EV charging infrastructure vulnerable to quantum computing. The focus remains squarely on the of EV charging and infrastructure with respect to PKC and is believed by the authors to complement the NIST SP 1800-38 Migration to Post-Quantum Cryptography. While the report is centered on infrastructure, there are implications to vehicles.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Advancing Grid Resilience through Smart Charge Management: Findings from Maryland’s Pilot

This report presents research findings from a four-year Smart Charge Management (SCM) pilot program conducted by Maryland’s largest electric utilities—Baltimore Gas and Electric (BGE), Potomac Electric Power Company (Pepco), and Delmarva Power & Light (DPL)—to evaluate strategies for optimizing electric vehicle (EV) charging loads and enhancing grid stability. Supported by the U.S. Department of Energy (DOE), Argonne National Laboratory collaborated with all project partners and examined the effectiveness of Time-of-Use (TOU) and Load Balancing (LB) strategies in managing peak demand, deferring costly infrastructure upgrades, and reducing grid constraints at the feeder level. Using charging data from over 4,600 EV drivers, the study analyzed SCM’s impact on the distribution systems of BGE and Pepco, which consists of over 2000 feeders. Unlike prior research that focused on system-wide trends or synthetic feeders, this analysis offers granular, feeder-level insights based on real-world operational data. It highlights how transformer density, load profiles, and infrastructure constraints influence smart charging performance. Results show feeder-level conditions play a crucial role in SCM effectiveness, with most feeders benefiting more from LB, while TOU-based SCM may be sufficient for others. By 2035, LB reduced peak charging loads by 27% on average, compared to 23% under TOU-based SCM, though some feeders saw reductions exceeding 35%, while others experienced minimal impact. Feeders with higher transformer utilization and limited capacity benefited more from LB, which more effectively distributed charging demand during off-peak hours. Beyond reducing grid constraints, SCM offers long-term operational and financial benefits. By shifting EV charging demand strategically, utilities can optimize asset utilization, delay infrastructure investments, and enhance grid performance. In terms of infrastructure upgrade deferrals, at the feeder level, LB consistently reduced peak charging loads and resulting infrastructure upgrade costs, particularly in high EV enrollment areas, decreasing the number of overloaded transformers by up to 35%, while TOU-based SCM achieved 20-30% reductions depending on feeder characteristics. At the system level, LB has the potential to defer total upgrade costs by $\$$186 million for BGE, compared to $\$$159 million under TOU-based SCM. For Pepco, TOU-based SCM performed slightly better, deferring upgrade costs by $\$$30 million, compared to $\$$29 million under LB. Section 4.5 reviews some of the system differences between BGE and Pepco. However, as EV adoption scales, TOU-based SCM will introduce secondary peak charging loads, reinforcing the need for more advanced, adaptive SCM approaches to prevent new grid challenges. As EV adoption continues to grow, feeder-level managed charging strategies will be essential for mitigating grid stress, improving infrastructure efficiency, and maintaining energy affordability for consumers. This report provides critical insights for utilities, Public Utility Commissions (PUCs), and state agencies on the role of feeder-specific smart charging in infrastructure planning, policy development, and grid modernization. The findings underscore the importance of tailored, data-driven SCM solutions that align with local grid conditions, ensuring a resilient, cost-effective transition to increasing EV adoption while safeguarding distribution system performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗