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At least 379 records · Page 21

Modeling charging infrastructure impact on the electric vehicle market in China

The plug-in electric vehicle (PEV) is deemed as a critical technological revolution, and the governments are imposing various vehicle policies to promote its development. Meanwhile, the market success of PEVs depends on many aspects. The study reported herein integrates one’s use of charging infrastructure at home, public place and workplace into the market dynamics analysis tool, New Energy and Oil Consumption Credits (NEOCC) model, to systematically assess the charging infrastructure (home parking ratio, public charging opportunity, and charging costs) impact on PEV ownership costs and analyze how the PEV market shares may be affected by the attributes of the charging infrastructure. Compared to the charging infrastructure, the impact of battery costs is incontrovertibly decisive on PEV market shares, the charging infrastructure is still non-negligible in the PEV market dynamics. The simulation results find that the public charging infrastructure has more effectiveness on promoting the PEV sales in the PEV emerging market than it does in the PEV mature market. However, the improvement of charging infrastructure does not necessarily lead to a larger PEV market if the charging infrastructure incentives do not coordinate well with other PEV policies. Besides, the increase of public charging opportunities has limited motivations on the growth of public PEV fleets, which are highly correlated to the number of public fast charging stations or outlets. It also finds that more home parking spaces can stimulate more sales of personal plug-in hybrid electric vehicles instead of personal battery electric vehicles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

HIVE™ [SWR-19-36]

The HIVE™ platform is a mobility services simulation platform developed to provide insight on the energy, infrastructure, service, and economic outcomes of various mobility as a service (MaaS) options. The HIVE platform takes a set of spatiotemporal travel origin-destination pairs and simulates the operation of a predefined mobility service fleet, incorporating request pooling, and various operational and charging behaviors. Hive specializes at modeling fleets of automated electric vehicles (AEVs) and can be used to site and size direct current fast charge (DCFC) stations and measure grid impacts of large-scale AEV fleets serving real-world MaaS trip demand (similar to taxis, Uber, Lyft, etc.). Potential outcomes from a Hive simulation include level of service, total vehicle miles traveled (VMT), deadheading (zero passenger) miles, simultaneous and total energy loads, average occupancy, and more. Hive is developed to generalize to new regions and can be customized to handle many scenarios and operating conditions.

Rames, Clement↗

Home charging for all: Techno-economic and life cycle assessment of multi-unit dwelling electric vehicle charging hubs

Ubiquitous electric vehicle adoption can drastically reduce greenhouse gas emissions (GHG) but will require equitable home charging infrastructure for all residences. Unlike single-family homes, Multi-Unit Dwellings (MUD) currently lack market share and access to nearby charging infrastructure partly due to expensive capital costs for reluctant residential property-owners. Further, this work evaluates the levelized cost of charging (LCOC) for Battery Electric Vehicles (BEVs) at MUD community charging hubs through a techno-economic analysis (TEA) that leverages real-world charging data and costs. Three different MUD charging types are investigated for a baseline and optimistic case: 1.9-kW Level 1 (L1), 6.6-kW Level 2 (L2), and 50-kW Direct Current Fast Charging (DCFC) stations under three different ownership models: resident, utility, and private company. Results demonstrate L1 and L2 chargers to be less expensive than the gasoline equivalent across the United States. Further, utility and private company ownership models, which avoid initial costs for residential property owners, result in a large LCOC premium for L1 and moderate LCOC premium for L2 relative to the resident ownership model. In contrast, DCFC is shown to be expensive for baseline scenarios but economical for optimistic scenarios especially under private company ownership. This work also performs a cradle to grave (C2G) life cycle assessment (LCA) of an average passenger BEV and gasoline conventional vehicle (CV) using yearly (grid mix & vehicle parameters), hourly (grid mix), and state-level (grid mix) resolution. Results show BEVs to have lower GHG emissions (-86% to-10%) than gasoline CVs in the contiguous U.S. Next, the system boundary of the TEA is extended to the total cost of ownership (TCO) of BEVs. The TCO is then coupled with the C2G GHG emissions to calculate the cost of GHG emissions reduction. Ultimately, the cost of GHG emissions reduction from MUD BEVs relative to gasoline CVs is shown to be negative for every scenario except baseline DCFC, meaning MUD BEV charging infrastructure can be a cost-effective endeavor to reduce GHG emissions.

33 ADVANCED PROPULSION SYSTEMS↗

Formal methods approach to the charging facility location problem for battery electric vehicles

Battery electric vehicles (BEVs) are becoming more prevalent as improvements in battery technology and energy management continue to be made. As the number of electric vehicles grows, the demand for fast-charging stations is expected to increase dramatically. Thus, building new charging station infrastructure efficiently will be key to reducing upfront costs while meeting consumer demands. In this work, we propose a method for choosing a set of charging station locations that are optimized based on a set of given common vehicle demand points. As part of this solution, we also offer a novel abstraction of the road network on which energy-efficient paths that account for charge-time delays may be found. The current algorithm chooses the optimal charging locations for a single agent which has a route objective specified using temporal logic. To demonstrate the proposed method, the running example shows how a charging station could be chosen for an electric delivery vehicle. Simulations were run on sample road networks with a given set of demand points to service and potential charging station locations to compare. The method is shown to successfully rank potential charging stations in terms of their expected average charging time cost.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Battery state-of-health diagnostics during fast cycling using physics-informed deep-learning

Rapid, in-situ Li-ion battery state-of-health (SOH) quantification is challenging. Li-ion battery aging can vary significantly with chemistry, operating conditions, cycling demands, electrode design, and operation history. As a cell ages, optimal and safe operating conditions need to be adapted to account for battery degradation by tracking critical aging modes such as loss-of-lithium-inventory (LLI), loss-of-active-material (LAM) in either electrode, and/or impedance rise. This manuscript describes a framework for identifying battery aging modes in-operando using fast-rate voltage charge/discharge responses. The framework uses a physically based Li-ion battery model to produce synthetic high-rate responses at aged states. The aging model is calibrated against experimental data from cells with different electrode loadings and cycled under a variety of fast-charging conditions (1 h, 15 min, 10 min, and 7 min charging). The synthetically generated high-rate responses at aged states are then used to train a deep-learning model to identify real cell state-of-health from fast charge/discharge battery voltage responses. The synthetically trained deep-learning model performance is validated by comparing to standard incremental capacity analysis and half-cell measurements. Finally, the framework demonstrates the benefits of using high-rate physics-based models to generate synthetic data for training deep-learning models.

25 ENERGY STORAGE↗

Charging Infrastructure Technologies: Development of a Multiport, >1 MW Charging System for Medium- and Heavy-Duty Electric Vehicles

Development of a Multiport, >1 MW Charging System for Medium- and Heavy-Duty Electric Vehicles project will develop research tools for a framework to design, optimize, and demonstrate key components of a multi-port 1+ MW medium-voltage connected charging system. The objectives of this effort are to develop strategies and technologies for multi-port 1+ MW grid-connected stations to recharge MD/HD electric vehicles at fast-charging travel plazas or at fleet depots; through industry engagement, charging station utilization and load analysis, grid impacts and interconnection analysis, detailed power electronics component design and controller demonstration, site and battery charge control design and controller demonstration; and charging connector design.

1+MW charging↗

Extracting LANSCE Macrobunch Charge from WNR Fast Pickoff [Slides]

Over several days around December 18, 2023, high-charge minipulses and macropulses were sent to Target 2, also known as the Blue Room. This report presents the calibration of a stripline-type current monitor known as the Fast Pickoff, and subsequent charge data for many of these shots. This effort utilized a fast oscilloscope, a nearby Bergoz current monitor, and some data-reduction techniques.

43 PARTICLE ACCELERATORS↗

Multivariate prognosis of battery advanced state of health via transformers

De-risking energy storage investments necessary to meet CO 2 reduction targets requires a deep understanding of the connections between battery health, design, and use. The historical definition of the battery state of health (SOH) as the percentage of current versus initial capacity is inadequate for this purpose, motivating an expanded SOH consisting of an interrelated set of descriptors including capacity, energy, ionic and electronic impedances, open-circuit voltages, and microstructure metrics. In this work, we introduce deep transformer networks for the simultaneous prognosis of 28 battery SOH descriptors using two cycling datasets representing six lithium-ion cathode chemistries, multiple electrolyte/anode compositions, and different charge-discharge scenarios. The accuracy of these predictions for battery life (with an unprecedented mean absolute error of 19 cycles in predicting end of life for a lithium-iron-phosphate fast-charging dataset) illustrates the promise of deep learning toward providing enhanced understanding and control of battery health.

25 ENERGY STORAGE↗

Rural EVSE Planning and Analysis

The dataset includes detailed anonymized public charging station usage from several rural stations on the ChargePoint and Shell Recharge Solutions (formerly Greenlots) networks situated in and around Athens, Ohio, a rural Appalachian community. Both Level 2 and DC fast charging stations are represented. Historical data in the set date back to 2019; additional data will be uploaded semiannually until the project's completion in 2023. Each charging session recorded includes information on date and time, location, charging station level, session duration, energy delivered, and fuel savings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Customer Experience at Public Charging Stations and Its Effects on the Purchase and Use of Electric Vehicles

To support the ChargeX Consortium’s mission to measure and significantly improve the reliability and usability of public charging infrastructure in North America, researchers in the consortium from the University of Washington (UW) reviewed the published literature and data from consortium members to evaluate how the consumer experience at public fast charging stations influences electric vehicle (EV) adoption.

33 ADVANCED PROPULSION SYSTEMS↗

Electrification Analysis: All Aboard America!

This one-page highlight details the key takeaways from a project that utilized NREL's Fleet Research, Energy Data, and Insights (FleetREDI) data analysis pipeline, an electrification analysis for the Bustang motorcoach fleet operated by All Aboard America! Holdings Inc. (AAA). NREL installed logging devices and collected operational data on nine 40-foot Bustang motorcoaches operating on fixed routes from May 2022 through August 2022. The analysis determined that partial fleet electrification may be feasible with electrified motorcoach options currently on the market. While this fleet faces significant challenges to electrification given current market options due to demanding range requirements and relatively limited charging opportunities, vehicles operating on the shorter, lower-grade routes along the I-25 corridor show more immediately available electrification potential. Increases in available battery capacity and the availability of fast-charging locations along I-70 routes are likely critical for electrification of the full fleet.

AAA↗

Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning

Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces. In this work, we develop a differentiable geometric deep learning (GDL) model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation and optimization towards fast charging battery electrolytes. In particular, we extend mixture thermodynamic and transport laws by creating GDL-learnable physical coefficients. We evaluate our model with mixture thermodynamics and ion transport properties, where we show improved prediction accuracy and model robustness of Diff-Mix than its purely data-driven variants. Furthermore, with a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps, via differentiable optimization built on DiffMix gradients. By combining GDL, mixture physics laws, and robotic experimentation, DiffMix expands the predictive modeling methods for chemical mixtures and enables efficient optimization in large chemical spaces.

25 - ENERGY STORAGE↗

Levelized Cost of Charging Electric Vehicles

This data set includes the levelized cost of charging (LCOC) and lifetime fuel cost savings (LFCS) values as reported in "Levelized Cost of Charging of Electric Vehicles in the United States." Values are reported at the state and national levels for battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). The data set also includes the four annual direct current fast charging (DCFC) station load profiles used to approximate the levelized cost of DCFC charging. Each profile provides 15-min resolved power requirements for one full year. Borlaug, B., Salisbury, S., Gerdes, M., and Muratori, M., Levelized Cost of Charging Electric Vehicles in the United States, Joule (2020), https://doi.org/10.1016/j.joule.2020.05.013.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Levelized Cost of Charging Electric Vehicles

This data set includes the levelized cost of charging (LCOC) and lifetime fuel cost savings (LFCS) values as reported in "Levelized Cost of Charging of Electric Vehicles in the United States." Values are reported at the state and national levels for battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). The data set also includes the four annual direct current fast charging (DCFC) station load profiles used to approximate the levelized cost of DCFC charging. Each profile provides 15-min resolved power requirements for one full year. Borlaug, B., Salisbury, S., Gerdes, M., and Muratori, M., Levelized Cost of Charging Electric Vehicles in the United States, Joule (2020), https://doi.org/10.1016/j.joule.2020.05.013.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Charge Transport in Zirconium-Based Metal–Organic Frameworks

Metal–organic frameworks (MOFs) are a class of crystalline porous materials characterized by inorganic nodes and multitopic organic linkers. Because of their molecular-scale porosity and periodic intraframework chemical functionality, MOFs are attractive scaffolds for supporting and/or organizing catalysts, photocatalysts, chemical-sensing elements, small enzymes, and numerous other functional-property-imparting, nanometer-scale objects. Notably, these objects can be installed after the synthesis of the MOF, eliminating the need for chemical and thermal compatibility of the objects with the synthesis milieu. Thus, postsynthetically functionalized MOFs can present three-dimensional arrays of high-density, yet well-separated, active sites. Depending on the application and corresponding morphological requirements, MOF materials can be prepared in thin-film form, pelletized form, isolated single-crystal form, polycrystalline powder form, mixed-matrix membrane form, or other forms. For certain applications, most obviously catalytic hydrolysis and electro- or photocatalytic water splitting, but also many others, an additional requirement is water stability. MOFs featuring hexa-zirconium(IV)-oxy nodes satisfy this requirement. For applications involving electrocatalysis, charge storage, photoelectrochemical energy conversion, and chemiresistive sensing, a further requirement is electrical conductivity, as embodied in electron or hole transport. As most MOFs, under most conditions, are electrically insulating, imparting controllable charge-transport behavior is both a chemically intriguing and chemically compelling challenge. Herein, we describe three strategies to render zirconium-based metal–organic frameworks (MOFs) tunably electrically conductive and, therefore, capable of transporting charge on the few nanometers (i.e., several molecular units) to few micrometers (i.e., typical dimensions for MOF microcrystallites) scale. The first strategy centers on redox-hopping between periodically arranged, chemically equivalent sites, essentially repetitive electron (or hole) self-exchange. Zirconium nodes are electrically insulating, but they can function as grafting sites for (a) redox-active inorganic clusters or (b) molecular redox couples. Alternatively, charge hopping based on linker redox properties can be exploited. Marcus’s theory of electron transfer has proven useful for understanding/predicting trends in redox-hopping based conductivity, most notably, in accounting for variations as great as 3000-fold depending on the direction of charge propagation through structurally anisotropic MOFs. In MOF environments, propagation of electronic charge via redox hopping is necessarily accompanied by movement of charge-compensating ions. Consequently, rates of redox hopping can depend on both the identity and concentration of ions permeating the MOF. In the context of electrocatalysis, an important goal is to transport electronic charge fast enough to match or exceed the inherent activity of MOF-based or MOF-immobilized catalysts. Bandlike electronic conductivity is the focus of an alternative strategy: one based on the introduction of molecular guests capable of forming donor–acceptor charge transfer complexes with the host framework. Theory again can be applied predictively to alter conductivity. A third strategy similarly emphasizes electronic conductivity, but it makes use of added bridges in the form of molecular oligomers or inorganic clusters that can then be linked to span the length of a MOF crystallite. For all strategies, retention of molecular-scale porosity is emphasized, as this property is key to many applications. Lastly, while our focus is on Zr-MOFs, the described approaches clearly are extendable to other MOF compositions, as has already been demonstrated, in part, in studies by others.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Non‐Linear Kinetics of The Lithium Metal Anode on Li 6 PS 5 Cl at High Current Density: Dendrite Growth and the Role of Lithium Microstructure on Creep

Abstract Interfacial instability, viz., pore formation in the lithium metal anode (LMA) during discharge leading to high impedance, current focusing induced solid–electrolyte (SE) fracture during charging, and formation/behaviour of the solid–electrolyte interphase (SEI), at the anode, is one of the major hurdles in the development of solid‐state batteries (SSBs). Also, understanding cell polarization behaviour at high current density is critical to achieving the goal of fast‐charging battery and electric vehicle. Herein, via in situ electrochemical scanning electron microscopy (SEM) measurements, performed with freshly deposited lithium microelectrodes on transgranularly fractured fresh Li6PS5Cl (LPSCl), the LiǀLPSCl interface kinetics are investigated beyond the linear regime. Even at relatively small overvoltages of a few mV, the LiǀLPSCl interface shows non‐linear kinetics. The interface kinetics possibly involve multiple rate‐limiting processes, i.e., ion transport across the SEI and SE|SEI interfaces, as well as charge transfer across the LiǀSEI interface. The total polarization resistance R P of the microelectrode interface is determined to be ≈ 0.8 Ω cm 2 . It is further shown that the nanocrystalline lithium microstructure can lead to a stable LiǀSE interface via Coble creep along with uniform stripping. Also, spatially resolved lithium deposition, i.e., at grain surface flaws, grain boundaries, and flaw‐free surfaces, indicates exceptionally high mechanical endurance of flaw‐free surfaces toward cathodic load (>150 mA cm −2 ). This highlights the prominent role of surface defects in dendrite growth.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

In-situ polymerized and crosslinked electrolytes with interchangeable Li/Na transport for battery applications

The next generation of batteries requires electrolytes with high conductivity, mechanical stability, good adhesion with electrodes, wide electrochemical windows, and scalability. The present study introduces a concept of doped quasi single-ion conducting copolymers based on methacrylate-(trifluoromethanesulfonyl)imide (TFSI) and vinyl ethylene carbonate which at room temperature are mechanically robust and display ionic conductivities of ~0.1 mS/cm. These electrolytes can be polymerized/crosslinked in-situ, making them easily implementable in current battery manufacturing technologies. They also allow for switching between Li + and Na + transport using simple chemistry procedures. To demonstrate their potential for battery applications, the newly developed Li conductors have been tested in symmetric cells, exhibiting overall impedance below 350 Ohm and plating/stripping stability up to 1 mA/cm 2 . Moreover, lithium metal batteries incorporating this electrolyte and high-voltage Lithium Nickel Manganese Cobalt Oxide (NMC) cathodes show good capacity retention (~79%) during charging and discharging for 80 cycles at C/10 rate and a Coulombic efficiency close to 100% in the entire measurement range. The compositional, mechanical and electrochemical versatility of these electrolytes opens new venues for the design of polymer-based batteries capable of fast charging and extended cycle life, aligning with the current global green energy storage strategies.

Polymer electrolytes↗

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↗