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At least 19 records

Multi-layered Energy Management Framework for Extreme Fast Charging Stations Considering Demand Charges, Battery Degradation, and Forecast Uncertainties

To achieve a cost-effective and expeditious charging experience for extreme fast charging station (XFCS) owners and electric vehicle (EV) users, the optimal operation of XFCS is crucial. It is however challenging to simultaneously manage the profit from energy arbitrage, the cost of demand charges, and the degradation of a battery energy storage system (BESS) under uncertainties. This paper, therefore, proposes a multi-layered multi-time scale energy flow management framework for an XFCS by considering long- and short-term forecast uncertainties, monthly demand charges reduction, and BESS life degradation. In the proposed approach, an upper scheduling layer (USL) ensures the overall operation economy and yields optimal scheduling of the energy resources on a rolling horizon basis, thereby considering the long-term forecast errors. A lower dispatch layer (LDL) takes the short-term forecast errors into account during the real-time operation of the XFCS. Per the latest research, monthly demand charges can be as high as 90% of the total monthly bills for EV fast charging stations; to this end, this paper takes the first attempt at the reduction of demand charges cost by considering the trade-off between the energy cost and monthly demand charges. Contrasting literature, this work allocates an energy reserve in the BESS stored energy to deal with the impact of short-term forecast errors on the optimized real-time operation of the XFCS. Moreover, degradation modeling considers the trade-off between short-term benefits and long-term BESS life degradation. As a result, case studies and a comparative analysis prove the efficacy of the proposed framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Balancing Charging Station Utilization and Throughput in Electric Vehicle Charging Stations with Queuing Theory

The rapid increase in electric vehicle (EV) adoption demands enhancements in the efficiency and adaptability of EV supply equipment (EVSE). Traditional EVSE systems often fail to optimize power delivery to meet the variable acceptance rates of EV batteries, resulting in significant energy wastage and reduced operational efficiency. This research addresses these challenges by integrating queuing theory with modular EVSE architectures, offering a dual strategy to optimize the operation of EV charging stations. A simulation model was developed to assess various configurations of charger capacities and outlet numbers. This model aimed to identify the optimal setup that maximizes station utilization while minimizing charging times and maximizing throughput. The model focused on charger capacities ranging from 50 to 250 kW and analyzed the different capacities’ effects on charging times and the number of vehicles served. The results indicate that a charger capacity of 125 kW is optimal, striking a balance between the charging time and the number of EVs served per hour, thus achieving the highest station utilization rate. This capacity allows for servicing a significant number of EVs with moderate increases in charging times. Lower capacities, although capable of serving more vehicles, lead to longer charging times and decreased throughput efficiency. The study underscores the effectiveness of combining queuing theory with flexible, modular charging systems that can dynamically adjust to EV charging demands.

Kumar, Praveen↗

Economic Storage Size Optimization for Electric Vehicle Extreme-Fast Charging Stations

En-route charging infrastructure for electric vehicles is critical to support transportation needs. These charging stations are likely to have high loads and especially sharp peak loads given fast charging capabilities needed to meet transportation schedules. In order to reduce both strain on distribution grid infrastructure and charging station operational costs, many stations are likely to employ behind the meter storage. This paper demonstrates a behind the meter storage sizing optimization that employs an open-source agent-based vehicle behavior model (BEAM) to determine the best sizing across many scenarios. This optimization and analysis is novel in that it examines how storage size impacts not only charging station cost and peak load, but also vehicle queue times. The optimization is also applied across a wide analysis region with sufficient diversity and numbers to provide novel statistical analysis of optimal sizes.

Aka, Julius↗

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↗

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↗

Grid impact analysis using controller-hardware-in-the-loop for high-power vehicle charging stations

A controller-hardware-in-the-loop (CHIL) architecture for the evaluation of grid impacts arising from high-power vehicle charging stations is presented in this paper. Unlike simulation-based studies, the proposed method can be used to capture the interactions of the grid and the charging load along with charger controllers in real time. The proposed method can be used to evaluate the impact of charging load on the grid in terms of voltage variations and line congestion. The proposed CHIL platform allows for de-risking the vehicle charging station deployment by simulating the complex interactions among all the components of a vehicle charging station - i.e., the grid, vehicle, and charger controller - in a realistic manner before using the charging station in a grid. Further, the proposed CHIL approach can be used to evaluate the voltage regulation causalities of the vehicle charging station. Experimental results are presented in the paper to illustrate the applicability of the proposed method in a laboratory environment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

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↗

Finding gaps in the national electric vehicle charging station coverage of the United States

Abstract The United States federal government has invested $7.5 billion into charging infrastructure, including the National Electric Vehicle Infrastructure Program, to build fast charging stations along designated highways for long-distance car travel. We develop a consecutive coverage metric to compute the percent of United States roads (traffic-weighted) that are consecutively accessible within 500 miles of each county. We answer (1) what the state of consecutive coverage is in each county and (2) what the increase in coverage is when designated highways receive fast chargers. In 2023, 10% of counties had at least 75% minimum viable coverage. We find that if all designated highways receive fast-charging stations, 94% of United States counties will reach at least 75% fast charger coverage. However, the remaining counties are rural. This demonstrates that federal funding for fast chargers will help connect most—but not all—counties to the national network of continuously accessible charging stations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Submerged AUV Charging Station

Autonomous Underwater Vehicles (AUVs) are becoming increasingly important for military surveillance and mine detection. Most AUVs are battery powered and have limited lifetimes of a few days to a few weeks. This greatly limits the distance that AUVs can travel underwater. Using a series of submerged AUV charging stations, AUVs could travel a limited distance to the next charging station, recharge its batteries, and continue to the next charging station, thus traveling great distances in a relatively short time, similar to the Old West “Pony Express.” One solution is to use temperature differences at various depths in the ocean to produce electricity, which is then stored in a submerged battery. It is preferred to have the upper buoy submerged a reasonable distance below the surface, so as not to be seen from above and not to be inadvertently destroyed by storms or ocean going vessels. In a previous invention, a phase change material (PCM) is melted (expanded) at warm temperatures, for example, 15 °C, and frozen (contracted) at cooler temperatures, for example, 8 °C. Tubes containing the PCM, which could be paraffin such as pentadecane, would be inserted into a container filled with hydraulic oil. When the PCM is melted (expanded), it pushes the oil out into a container that is pressurized to about 3,000 psi (approx equals 20.7 MPa). When a valve is opened, the high-pressure oil passes through a hydraulic motor, which turns a generator and charges a battery. The low-pressure oil is finally reabsorbed into the PCM canister when the PCM tubes are frozen (contracted). Some of the electricity produced could be used to control an external bladder or a motor to the tether line, such that depth cycling is continued for a very long period of time. Alternatively, after the electricity is generated by the hydraulic motor, the exiting low-pressure oil from the hydraulic motor could be vented directly to an external bladder on the AUV, such that filling of the bladder causes the AUV to rise, and emptying of the bladder allows the AUV to descend. This type of direct buoyancy control is much more energy efficient than using electrical pumps in that the inefficiencies of converting thermal energy to electrical energy to mechanical energy is avoided. AUV charging stations have been developed that use electricity produced by waves on floating buoys and that use electricity from solar photovoltaics on floating buoys. This is the first device that has absolutely no floating or visible parts, and is thus impervious to storms, inadvertent ocean vessel collisions, or enemy sabotage.

Jones, Jack A.↗

Public electric vehicle charging station utilization in the United States

The utilization of electric vehicle (EV) charging equipment is a key driver of charging station economics, but current trends and factors related to the utilization of public charging infrastructure in the United States are not well understood. This study analyzes EV charging data from 3,705 nationwide public Level 2 (L2) and direct current fast charging (DCFC) stations over 2.5 years (2019-2022), observing utilization patterns over time. Regression analysis is used to assess the relationships between station utilization and several contextual and environmental factors. We conclude that local EV adoption is a strong indicator of utilization; L2 station utilization decreases with the size of the local charging network, while DCFC stations are less affected; and increased charging power has a greater effect on utilization for DCFC stations than L2. This study fills a critical research gap by reporting updated public charging station utilization statistics and analysis for the U.S. market.

33 ADVANCED PROPULSION SYSTEMS↗

Electric Vehicle Charging Stations as a Climate Change Mitigation Strategy

In order to facilitate the use of electric vehicles at NASA Langley Research Center (LaRC), charging stations should be made available to LaRC employees. The implementation of charging stations would decrease the need for gasoline thus decreasing CO2 emissions improving local air quality and providing a cost savings for LaRC employees. A charging station pilot program is described that would install stations as the need increased and also presents a business model that pays for the electricity used and installation at no cost to the government.

Cave, Bridget↗

Techno-Economic Assessments of Second-Life Batteries for Electric Vehicle Charging Stations

When electric vehicle (EV) batteries degrade below a certain capacity, they may no longer be suitable for automotive use but can be repurposed as second-life batteries (SLBs) for other applications, such as EV charging stations. When integrated with photovoltaic (PV) systems, SLB can store surplus solar energy, reducing reliance on the grid and lowering operational costs. This paper presents a novel techno-economic assessment framework for deploying SLBs in combination with PV in grid-connected EV charging stations. The proposed framework integrates the value proposition, charging station operation, optimal dispatch strategies, battery degradation modeling, input data requirements, and detailed procedures for generating key economic performance metrics. Insightful analyses are performed to assess the performance of SLBs in comparison to new batteries across various cost scenarios. The results indicate that SLBs become financially attractive when their cost is 40% or lower than new batteries.

energy storage↗

Siting and sizing of public–private charging stations impacts on household and electric vehicle fleets

To facilitate the provision of electric vehicle charging stations (EVCS) in urban areas, this study investigates the benefits of co-locating fleet-owned chargers with public charging stations to enable construction incentives and cord-sharing cost savings. Shared EVCS can serve charging demand from both user types: private (household) EV owners and those managing fleet vehicles – like shared and fully automated EV (SAEV) fleets. Using POLARIS to simulate all person-travel across the 6-county Austin, Texas region, new EVCS were sited and sized with DC fast-charging (DCFC) plugs to lower operating and construction costs while providing public + private (PP) service across an 81-square-mile core geofence (where 200 SAEVs were active) over 24-hour days. When co-location is permitted, 115 DCFC cords were added to the 23 existing (publicly available) stations to enable SAEVs and household EVs (HHEVs) charging access, within the geofence. Each 250-mile-range SAEV was simulated to travel an average of 330 miles per day, serve over 92 person-trips, and recharge 2.7 times a day (for 2.4 h per session). The new DCFC plugs were primarily added to public EVCS at shopping centers and schools, and in residential settings along freeways. The average plug served 4.8 EVs per day. Most co-located PP EVCS permitted immediate (no-wait) charging, except for 2 stations along freeways that averaged 8 min of wait time to begin charging. In conclusion, the co-location strategy lowered fleet owners’ initial EVCS construction costs by 12 % (thanks to cord-sharing to avoid cord duplication), while reducing SAEV wait times to just 3.1 min (versus 10.7 min if SAEV managers had to build and operate their own EVCS).

EV charging modeling↗

EVI-LOCATE: One Stop Solution for Estimating Cost of Installing EV Charging Stations

EVI-LOCATE (Electric Vehicle Infrastructure-Locally Optimized Cost Assessment Tool and Estimator) is a site assessment tool to estimate costs to install EV charging stations. EVI-LOCATE enables users to generate site-specific, user-specific, and location-specific EV charging station installation designs and cost estimates. The tool integrates National Electrical Code, deep-learning pixel classification algorithm, and component-level costs to estimate the costs.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

A Hybrid Fuel Cell and Battery Storage Power Management for Grid-Interactive EV Charging Station

With the increasing adoption of renewable energy sources in grid-interactive Electric Vehicle (EV) charging stations, the role of energy storage systems has become critical. While large energy storage systems have mitigated the intermittency of renewable energy, integrating multi-source energy management with prioritized charging can further enhance the reliability of charging stations (CS). This paper presents a decentralized energy management (DEM) approach combining battery energy storage (BES) and fuel cell (FC) systems using a rule-based line resistance correction droop (LRCD) control technique. The proposed droop control dynamically adjusts the gain to balance the state-of-charge (SoC) of the BES, enhancing power support longevity and improving battery life under varying capacity conditions by reducing current stress. Additionally, the paper addresses the challenges of using fuel cells in linear regions to optimize efficiency and manage various charging scenarios. The CS integrates unity power factor grid interaction, and power support for auxiliary loads, maintaining harmonic distortion within 5% during grid islanding. The approach evaluates DC bus voltage regulation under various scenarios of PV array power fluctuations and dynamic load variations, in both grid-connected and standalone operations. In conclusion, the proposed control strategy is validated on a laboratory prototype through various dynamic load variation and grid islanding scenarios.

Khalid, Mohd [Oak Ridge National Laboratory (ORNL)↗

Improving Resiliency in Planning MW-Scale Medium and Heavy Duty EV Charging Stations Considering TSCOTS Optimization

Electrification of heavy-duty (HD) vehicles marks an important milestone and technical challenge in the electric vehicle (EV) industry and the public grid. However, implementing EV charging at this scale will necessitate that traditional truck stops be updated with EV charging infrastructure that could represent 10's of MW in electricity consumption. Furthermore, as the transportation sector is represented as critical infrastructure, supporting resiliency considerations in EV charging infrastructure will be critical. This paper proposes an optimization-based approach for optimally designing a MW-scale microgrid charging network. This approach transforms conventional designed truck stops into a reliable HDEV charging stations capable of overnight slow charging and 30-minute to 1 hour fast charging. Using a mixed-integer linear program formulation blending capacity planning and reliability constraints, an optimal network configuration can be solved for a proposed EV charging station that includes photovoltaic and battery energy storage capabilities.

Ponce, Moises [University of Tennessee, Knoxville ↗

A Stochastic Framework for Estimating Load Profiles at EV Fast Charging Stations

This paper formulates a methodology for estimating the average daily load profiles of EV fast charging stations over a planning horizon of five to ten years. The developed methodology uses historic vehicle registration data, state-level EV adoption targets, seasonal driving patterns, local demographics, competition, and traffic volume information to predict average station usage. Through Monte Carlo simulations, an average daily load profile is obtained for each month in the planning horizon, and prediction uncertainty is quantified. The proposed framework will facilitate the accurate estimation of energy and demand costs incurred by the charging station over the planning period, thereby informing return-on-investment calculations.

Biswas, Shuchismita↗

National Dataset of EV Charging Stations With Estimates of Load and Vehicle Throughput

Current data from the AFDC provide locations and many details about EV charging stations, but not estimates of their peak loads or the number of vehicles they can accommodate. This dataset will augment the AFDC charging station locations with estimates of transmission load and vehicle throughput based on engineering specifications of the chargers, charging patterns based on vehicle types, battery capacities, and user behavior.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗