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DOE OSTI2023

Design, Modeling, and Testing of a Novel Inductor for Electric Vehicles: Iron Nitride Soft Magnetic Composites

New technology for electric vehicles (EVs) must meet the requirements of higher energy usage, lower costs, and more sustainable source materials. One promising material for EV power system components is iron nitride (IN) soft magnetic composites (SMCs) because of their competitive magnetic properties and high abundance of the source materials. As part of an ongoing program at Sandia National Laboratories, this project focused on using computer modeling to optimize the prototyping process for an iron nitride SMC toroidal inductor to reach a target inductance of 600 μH. Four inductors with different combinations of wiring (26 AWG and 20 AWG) and vol% loading of iron nitride (65 vol% and 50 vol%) were fabricated at Cal Poly and characterized using an LCR meter. These inductors were also modeled using COMSOL Multiphysics™ with the Magnetic Fields module. The inductance data from the experiment and the model show that the 65 vol% IN prototypes and models agree with about 8% difference, while the 50 vol% IN samples show about a 9% difference between the prototype and the model. These results suggest that the model can predict inductance with both accuracy and precision with low confidence for the given sample size of four. An additional parameter of AC resistance is studied but the AC resistance results from the inductors and from the model generally do not agree closely, suggesting that the current model used in the project does not fully capture the mechanisms behind AC resistance of the inductor. With the focus of the project on inductance, the percent difference results of less than 9% across the four inductors that were tested increases confidence in the model’s predictive capabilities for inductance only. Using the inductance results from both the model and experiment, the final suggested inductor design is a 65 vol% core with 150 windings of 20 AWG wire that is 8 cm across and 1.5 cm tall to reach the inductance goal of 600 μH based on analysis using the optimized COMSOLTM model.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Axion pulsarscope

Electromagnetic fields surrounding pulsars may source coherent ultralight axion signals at the known rotational frequencies of the neutron stars, which can be detected by laboratory experiments (e.g., pulsarscopes). As a promising case study, we model axion emission from the well-studied Crab pulsar, which would yield a prominent signal at 𝑓 ≈ 29.6 Hz regardless of whether the axion contributes to the dark matter abundance. We estimate the relevant sensitivity of future axion dark matter detection experiments such as DMRadio-GUT, Dark SRF, and CASPEr, assuming different magnetosphere models to bracket the uncertainty in astrophysical modeling. For example, depending on final experimental parameters, the Dark SRF experiment could probe axions with any mass 𝑚 𝑎 ≪ 10 −13 eV down to 𝑔 𝑎⁢𝛾⁢𝛾 ∼3 × 10 −13 GeV −1 with one year of data and assuming the vacuum magnetosphere model. These projected sensitivities may be degraded depending on the extent to which the magnetosphere is screened by charge-filled plasma. The promise of pulsar-sourced axions as a clean target for direct detection experiments motivates dedicated simulations of axion production in pulsar magnetospheres.

axions↗

DOE OSTI2024

Fourier Analysis and Loss Modeling for Inductive Wireless Electric Vehicle Charging with Reduced Stray Field

With the growth of electric vehicle (EV) popularity, different charging options to increase user convenience and reduce charging times are being considered and researched. Among these, inductive wireless power transfer (WPT) systems for EVs are being designed to meet specifications such as stray field, power level, efficiency, misalignment tolerance, and ground clearance, which are all heavily influenced by the coil geometry. The proposed Fourier Analysis Method (FAM) is an analytical method to directly design coil geometries to meet stray field and power level requirements through an optimization of Fourier basis function coefficients. The outputs of the optimization are complex, planar coil geometries that meet the power level and stray field constraints with minimized loss factors. Contours of these potentials determine the coil conductor paths and loss models predict the system efficiency and performance over misalignment. The Fourier representation of the geometry is used to conveniently calculate the coupling over misalignment, external proximity effect loss, and ferrite loss. A 6.6 kW prototype WPT system with low stray field and high efficiency is built from the optimization results to validate the models and showcase the usefulness of the FAM design approach.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

Battery Energy Storage Scenario Analyses Using the Lithium-Ion Battery Resource Assessment (LIBRA) Model

Meeting aggressive carbon emission goals will entail widespread deployment of renewable sources of electricity. Because these sources are variable, there is a need to develop scalable energy storage technologies. The U.S. Department of Energy is supporting efforts to increase U.S. manufacturing and recycling capabilities for LIBs and to decrease costs of stationary storage batteries. Many factors influence the domestic manufacturing and cost of stationary storage batteries, including availability of critical raw materials (lithium, cobalt, and nickel), competition from various demand sectors (consumer electronics, vehicles, and battery energy storage), resource recovery (recycling), government policies, and learning in the industry, among other factors. Understanding how these factors interact and identifying synergies and bottlenecks is important for developing effective strategies for the LIB stationary energy storage system. We developed the Lithium-Ion Battery Resource Analysis (LIBRA) model as a tool to help stakeholders better understand the following types of questions: What are the roles of R&D, industrial learning, and scaling of demand in lowering the cost of battery energy storage system production? How do the intersections between the EV and stationary storage sectors affect the battery supply chain? For various stationary storage and EV penetration scenarios, what volumes of critical materials might be required and what role can resource recovery play? What does expected demand for both EVs and stationary storage portend for mineral resources and overall mineral scarcity? The LIBRA model is developed using a System dynamics (SD) modeling approach to represent interactions across the segments of the battery materials supply chain. System dynamics models can capture the complex interactions and feedback between the various system components that influence supply and demand. The LIBRA model is comprised of several interacting modules that represent specific portions of the LIB supply chain. The model tracks the buildout of the domestic LIB industry over time (2020 - 2050) and in the context of competing demands for raw materials, recycling, and markets for LIBs. The LIBRA model represents major systemic feedback loops and delays across the supply chain. This report provides a complete documentation for the LIBRA model, including model assumptions, data, scenario analysis results, and sensitivity analysis of the model's input space.

25 ENERGY STORAGE↗

NASA NTRS1994

X-ray variability and period determinations in the eclipsing polar DP Leonis

We present an analysis of our ROSAT position sensitive proportional counter (PSPC) observations on the eclipsing magnetic cataclysmic variable DP Leo. The soft X-ray spectrum is modeled by a blackbody of kT = 24.8(sup +2.6 sub -8.1) eV. Severe limits are placed upon the flux from any hard bremsstrahlung component. A strong soft X-ray excess, with respect to hard X-ray emission, is found. The soft X-ray blackbody luminosity is larger than both the cyclotron and bremsstrahlung luminosities. An upper limit of 500 pc is obtained for the system's distance based upon the X-ray absorption (N(sub H) less than 5 x 10(exp 19)/sq cm) and an estimate of 260(sup +150 sub -100) pc is determined from a published measurement of the secondary's flux. For the derived blackbody fit, the bolometric luminosity is found to be L(sub bb, bol) = 1.4(sup +7.1 sub -0.3) x 10(exp 31)(d/260 pc)(exp 2) ergs/s. Absorption by the accretion stream produces an intensity dip prior to each eclipse. Extreme variability in the shape of the light curve from eclipse to eclipse demonstrates that changes in the rate of accretion onto the white dwarf, the sizes of accretion filaments, or variations in the location or amount of absorbing matter in the system occur on timescales shorter than the orbital period (89.8 minutes). No evidence exists for accretion onto the stronger (59 MG) magnetic pole in the ROSAT data. A new ephemeris is presented for the eclipse of the white dwarf emission region by the secondary star and another is produced for the orbital conjunction of the two components. The rotation of the white dwarf is shown to be faster than the orbital period by (5.3 +/- 1.1) x 10(exp -3) s. The origin of the asynchronous rotation may be activity cycle included orbital period variations or oscillations of the white dwarf's main pole about an equilibirium position. The accretion stream is modeled assuming that disruption of the stream along magnetic field lines occurs close to the white dwarf. The ROSAT intensity dip is explained using this model and the results are shown to be consistent with contemporaneous optical observations.

Robinson, Craig R.↗

DOE OSTI2021

Cesium-Coated Halide Perovskites as a Photocathode Material: Modeling Insights

Photocathodes emit electrons when illuminated, a process utilized across many technologies. Cutting-edge applications require a set of operating conditions that are not met with current photocathode materials. Meanwhile, halide perovskites have been studied extensively and have shown a lot of promise for a wide variety of optoelectronic applications. Well-documented halide perovskite properties such as inexpensive growth techniques, improved carrier mobility, low trap density, and tunable direct band gaps make them promising candidates for next-generation photocathode materials. In this study, we use density functional theory to explore the possible application of pure inorganic perovskites (CsPbBr 3 and CsPbI 3 ) as photocathodes. It is determined that the addition of a Cs coating improved the performance by lowering the work function anywhere between 1.5 and 3 eV depending on the material, crystal surface, and surface coverage. A phenomenological model, modified from that developed by Gyftopoulos and Levine, is used to predict the reduction in work function with Cs coverage. The results of this work aim to guide the further experimental development of Cs-coated halide perovskites for photocathode materials.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2022

Machine learning-assisted elucidation of CD81–CD44 interactions in promoting cancer stemness and extracellular vesicle integrity

Tumor-initiating cells with reprogramming plasticity or stem-progenitor cell properties (stemness) are thought to be essential for cancer development and metastatic regeneration in many cancers; however, elucidation of the underlying molecular network and pathways remains demanding. Combining machine learning and experimental investigation, here we report CD81, a tetraspanin transmembrane protein known to be enriched in extracellular vesicles (EVs), as a newly identified driver of breast cancer stemness and metastasis. Using protein structure modeling and interface prediction-guided mutagenesis, we demonstrate that membrane CD81 interacts with CD44 through their extracellular regions in promoting tumor cell cluster formation and lung metastasis of triple negative breast cancer (TNBC) in human and mouse models. In-depth global and phosphoproteomic analyses of tumor cells deficient with CD81 or CD44 unveils endocytosis-related pathway alterations, leading to further identification of a quality-keeping role of CD44 and CD81 in EV secretion as well as in EV-associated stemness-promoting function. CD81 is coexpressed along with CD44 in human circulating tumor cells (CTCs) and enriched in clustered CTCs that promote cancer stemness and metastasis, supporting the clinical significance of CD81 in association with patient outcomes. Our study highlights machine learning as a powerful tool in facilitating the molecular understanding of new molecular targets in regulating stemness and metastasis of TNBC.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Revealing EDL-driven reduction mechanisms in binary, ternary, and quaternary fluorinated electrolytes via an integrated MD–DFT–ML framework

Accurately predicting solid electrolyte interphase (SEI) formation requires explicitly resolving the electric double layer (EDL) structure, which deviates significantly from that of the bulk electrolyte. Although an established molecular dynamics (MD) and Density Functional Theory (DFT) framework can model SEI formation by evaluating reduction reactions of local clusters in the EDL, it suffers from a combinatorial computational bottleneck. To overcome this limitation, we introduce a machine-learning-accelerated simulation workflow (MD–DFT–ML), integrating a gradient-boosted regression model trained on EDL composition data to efficiently predict reduction potentials. We apply this framework to seven fluorinated electrolytes comprising fluorinated anions, a fluorinated ester solvent, two types of diluent (ion-solvating ester vs. non-solvating ether), and an FEC additive. The analysis shows that the EDL selectively accumulates cation-binding species; consequently, the non–cation-binding ether diluent rarely enters the EDL and makes minimal contributions to SEI formation. DFT calculations on statistically representative EDL clusters provide reduction potentials and fluorine-release pathways, while the ML model, which substantially reduces the DFT workload, predicts cluster reduction energies with a mean absolute error of 0.1 eV. The combined MD–DFT–ML approach also quantifies contributions from different sources to LiF formation in the SEI. This methodology establishes a generalizable route for multiscale modeling electrolyte and interphase design for next-generation electrochemical energy-storage systems.

DFT-MD-ML workflow↗

NASA NTRS1991

Test of the decaying dark matter hypothesis using the Hopkins Ultraviolet Telescope

Sciama's hypothesis that the dark matter associated with galaxies, galaxy clusters, and the intergalactic medium consists of tau neutrinos of rest mass 28-30 eV whose decay generates ultraviolet photons of energy roughly 14-15 eV, has been tested using the Hopkins Ultraviolet Telescope flows aboard the Space Shuttle Columbia. A straightforward application of Sciama's model predicts that a spectral line from neutrino decay photons should be observed from the rich galaxy cluster Abell 665 with an SNR of about 30. No such emission was detected. For neutrinos in the mass range 27.2-32.1 eV, the observations set a lower lifetime limit significantly greater than Sciama's model requires.

Davidsen, A. F.↗

DOE OSTI2026

Development Status of the KIPM Detector Consortium

A kinetic inductance phonon-mediated detector is a calorimeter that uses kinetic inductance detectors (KIDs) to read out phonon signals from the device substrate. We have established a consortium comprising university and national lab groups dedicated to advancing the state of the art in these detectors, with the ultimate goal of designing a detector with sub-eV threshold on energy deposited in the substrate, enabling searches for both light dark matter and low-energy neutrino interactions. This consortium brings together experts in KID design, phonon and quasi-particle dynamics, and noise modeling, along with specialized fabrication facilities, test platforms, and unique calibration capabilities. Recently, our consortium has demonstrated a resolution on energy absorbed by the sensor of 2.1 eV, the current record for phonon-sensitive KIDs. However, due to ∼ 1% phonon collection efficiency, the overall detector resolution was limited to 320 eV. Finally, the current focus of the consortium is modeling and improving the phonon collection efficiency and implementing low-T c superconductors, both of which serve to improve the overall energy resolution and threshold of the detectors.

Temples, Dylan J. [Fermi National Accelerator Labo↗

DOE OSTI2023

Comparison of Socio-Technical Threat Models

Given the adoption of emerging technologies and the increasing complexity of managing such systems with a lifecycle much shorter than that of critical infrastructure systems, there is a practical need to be able to analyze sociotechnical dependencies and their associated evolving risks. Threat models based on social influence techniques can be used to implement adversarial tactics analogous to the cyber kill chain and attested to within the MITRE ATT&CK for ICS framework including Initial Access, Persistence, Collection, and Impact. Furthermore, as with cyber disruptions, the impact of social influence threat models can have an asymmetric impact that is not spatially-localized. Finally, unlike cyber attacks with a reasonably short duration (ransomware takes days to months), social influence based attacks have the potential to persist for much longer as they are based on long-term strategic infrastructure investments within the private sector. Given the increased importance of electric vehicle charging stations as a long-term, strategic infrastructure investment within the Energy and Transportation Sectors, we provide initial results that compare the impact of a Loss of Availability (T0826) realized through cyber and social influence based threat models. The analysis employs techniques from automated reasoning and measures of network complexity to understand evolving dominance of EV payment and charging networks within geographic region of interest. Within this context, we compare the impact of a loss of availability due to ransomware versus that of loss of support due to a merger and acquisition. Results across several different metro areas will be provided.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Quantifying the Financial Impacts of Electric Vehicles on Utility Ratepayers and Shareholders [Slides]

Widespread electric vehicle (EV) adoption is critical for meeting economy-wide decarbonization goals and, as a result, states are considering enabling policies and rate designs to accelerate EV deployment. EVs can provide possible financial upside to electric utilities and ratepayers in several ways. For example, from the utility perspective, EVs could drive increased electricity sales and new earnings opportunities through increased capital investments. From the ratepayer perspective, increased electric loads from EVs could reduce average all-in retail rates. The degree to which there are net benefits or costs to shareholders and/or ratepayers depends on how EVs are integrated and managed through enabling grid investments and charging strategies. Using Berkeley Lab’s Financial Impacts of Distributed Energy Resources (FINDER) model that mimics the electric utility investment planning and ratemaking processes, we estimate the utility earnings and customer rate impacts of EVs using a bookend approach of “managed” (i.e., best case) and “mismanaged” (i.e., worst case) charging strategies for a generic summer-peaking, investor-owned, and vertically integrated utility. The analysis also examines the sensitivity of results to different assumptions of EV deployment characteristics, EV impacts on retail electricity sales, incremental distribution system costs, EV charging location, and utility EV enablement costs (i.e., utility costs to invest in EV charging, controls, and communication to deliver and administer EV programs). The results are intended to inform EV policies and deployment strategies that maximize utility system benefits and minimize ratepayer costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Search for coherent elastic neutrino-nucleus scattering at a nuclear reactor with CONNIE 2019 data

The Coherent Neutrino-Nucleus Interaction Experiment (CONNIE) is taking data at the Angra 2 nuclear reactor with the aim of detecting the coherent elastic scattering of reactor antineutrinos with silicon nuclei using charge-coupled devices (CCDs). In 2019 the experiment operated with a hardware binning applied to the readout stage, leading to lower levels of readout noise and improving the detection threshold down to 50 eV. The results of the analysis of 2019 data are reported here, corresponding to the detector array of 8 CCDs with a fiducial mass of 36.2 g and a total exposure of 2.2 kg-days. The difference between the reactor-on and reactor-off spectra shows no excess at low energies and yields upper limits at 95% confidence level for the neutrino interaction rates. In the lowest-energy range, 50 − 180 eV, the expected limit stands at 34 (39) times the standard model prediction, while the observed limit is 66 (75) times the standard model prediction with Sarkis (Chavarria) quenching factors.[graphic not available: see fulltext]

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

The Identity and Chemistry of C 7 H 7 Radicals Observed during Soot Formation

Here we used aerosol mass spectrometry coupled with tunable synchrotron photoionization to measure radical and closed-shell species associated with particle formation in premixed flames and during pyrolysis of butane, ethylene, and methane. We analyzed photoionization (PI) spectra for the C 7 H 7 radical to identify the isomers present during particle formation. For the combustion and pyrolysis of all three fuels, the PI spectra can be fit reasonably well with contributions from four radical isomers: benzyl, tropyl, vinylcyclopentadienyl, and o-tolyl. Although there are significant experimental uncertainties in the isomeric speciation of C7H 7 , the results clearly demonstrate that the isomeric composition of C 7 H 7 strongly depends on the combustion or pyrolysis conditions and the fuel or precursors. Fits to the PI spectra using reference curves for these isomers suggest that all of these isomers may contribute to m/z 91 in butane and methane flames, but only benzyl and vinylcyclopentadienyl contribute to the C 7 H 7 isomer signal in the ethylene flame. Only tropyl and benzyl appear to play a role during pyrolytic particle formation from ethylene, and only tropyl, vinylcyclopentadienyl, and o-tolyl appear to participate during particle formation from butane pyrolysis. There also seems to be a contribution from an isomer with an ionization energy below 7.5 eV for the flames but not for the pyrolysis conditions. Kinetic models with updated and new reactions and rate coefficients for the C 7 H 7 reaction network predict benzyl, tropyl, vinylcyclopentadienyl, and o-tolyl to be the primary C 7 H 7 isomers and predict negligible contributions from other C 7 H 7 isomers. These updated models provide better agreement with the measurements than the original versions of the models but, nonetheless, underpredict the relative concentrations of tropyl, vinylcyclopentadienyl, and o-tolyl in both flames and pyrolysis and overpredict benzyl in pyrolysis. Our results suggest that there are additional important formation pathways for the vinylcyclopentadienyl, tropyl, and o-tolyl radicals and/or loss pathways for the benzyl radical that are currently unaccounted for in the present models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Machine learning to accelerate screening for Marcus reorganization energies

Understanding and predicting the charge transport properties of π-conjugated materials is an important challenge for designing new organic electronic devices, such as solar cells, plastic transistors, light-emitting devices, and chemical sensors. A key component of the hopping mechanism of charge transfer in these materials is the Marcus reorganization energy which serves as an activation barrier to hole or electron transfer. While modern density functional methods have proven to accurately predict trends in intramolecular reorganization energy, such calculations are computationally expensive. In this work, we outline active machine learning methods to predict computed intramolecular reorganization energies of a wide range of polythiophenes and their use toward screening new compounds with low internal reorganization energies. Our models have an overall root mean square error (RMSE) of ±0.113 eV, but a much smaller RMSE of only ±0.036 eV on the new screening set. Since the larger error derives from high-reorganization energy compounds, the new method is highly effective to screen for compounds with potentially efficient charge transport parameters.

14 SOLAR ENERGY↗

DOE OSTI2024

A comprehensive study on three typical photoacid generators using photoelectron spectroscopy and ab initio calculations

Conducting a comprehensive molecular-level evaluation of a photoacid generator (PAG) and its subsequent impact on lithography performance can facilitate the rational design of a promising 193 nm photoresist tailored to specific requirements. In this study, we integrated spectroscopy and computational techniques to meticulously investigate the pivotal factors of three prototypical PAG anions, p-toluenesulfonate (pTS - ), 2-(trifluoromethyl)benzene-1-sulfonate (TFMBS - ), and triflate (TF - ), in the lithography process. Our findings reveal a significant redshift in the absorption spectra caused by specific PAG anions, attributed to their involvement in electronic transition processes, thereby enhancing the transparency of the standard PAG cation, triphenylsulfonium (TPS + ), particularly at ~193 nm. Furthermore, the electronic stability of PAG anions can be enhanced by solvent effects with varying degrees of strength. Here we observed the lowest vertical detachment energy of 6.6 eV of pTS - in PGMEA solution based on the polarizable continuum model, which prevents anion loss at 193 nm lithography. In addition, our findings indicate gas-phase proton affinity values of 316.4 kcal/mol for pTS - , 308.1 kcal/mol for TFMBS - , and 303.2 kcal/mol for TF - , which suggest the increasing acidity strength, yet even the weakest acid pTS - is still stronger than strong acid HBr. The photolysis of TPS + -based PAG, TPS + ·pTS - , generated an excited state leading to homolysis bond cleavage with the lowest reaction energy of 83 kcal/mol. Overall, the PAG anion pTS - displayed moderate acidity, possessed the lowest photolysis reaction energy, and demonstrated an appropriate redshift. These properties collectively render it a promising candidate for an effective acid producer.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Activation of methane by U + studied by guided ion beam tandem mass spectrometry and quantum chemistry

Reaction pathways of all products formed in the U + + CH 4 (CD 4 ) reaction were explored as a function of kinetic energy using guided ion beam tandem mass spectrometry and quantum chemical calculations. UH + , UC + , UCH + , UCH 2 + , and UCH 3 + (and their perdeuterated analogues) are formed in endothermic reactions. In both systems, the UCH 2 + (UCD 2 + ) dehydrogenated product was the dominant product in the low-energy region, whereas the UH + (UD + ) hydride product became predominant at high energies. The kinetic energy behavior of the various products is consistent with a common intermediate of H–U + –CH 3 (D–U + –CD 3 ). Here, the kinetic energy dependence of all product cross sections was modeled to obtain experimental bond dissociation energies at 0 K (in eV): D 0 (U + –H) = 2.42 ± 0.10, D 0 (U + –C) = 3.95 ± 0.12, D 0 (U + –CH) = 4.91 ± 0.09, D 0 (U + –CH 2 ) = 4.11 ± 0.04, and D 0 (U + –CH 3 ) = 2.41 ± 0.09. Quantum chemical calculations using the UCCSD(T) and UB3LYP approaches with the cc-pwCVXZ-PP basis set with MDF-60 pseudopotential for U + and the aug-cc-pCVXZ and aug-cc-pVXZ (X = T, Q) basis set for carbon and hydrogen, respectively, validate the experimental bond dissociation energies and outline the potential energy surface for all reactions observed. In addition, spin–orbit corrections of the bond energies for all products were calculated at a CASSCF-CASPT2-RASSI level.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Energy Cost Analysis and Operational Range Prediction Based on Medium- and Heavy-Duty Electric Vehicle Real-World Deployments across the United States

While the market for medium- and heavy-duty battery-electric vehicles (MHD EVs) is still nascent, a growing number of these vehicles are being deployed across the U.S. This study used over 2.3 million miles of operational data from multiple types of MHD EVs across various regions and operating conditions to address knowledge gaps in total cost of ownership and operational range. First, real-world energy cost savings were determined: MHD fleets should experience energy cost savings each year from 2021 to 2035, regardless of vehicle platform, with the greatest savings seen in transit buses (up to USD 4459 annually) and HD trucks (up to USD 3284 annually). Second, to help fleets across various geographies throughout the U.S. assess the suitability of EVs for their year-round operating needs, operational range was modeled using the XGBoost algorithm (R2: 70%) given 22 input features relevant to vehicle efficiency. Finally, this paper recommends (1) that MHD fleets apply energy-saving practices to minimize the impacts of cold temperatures and high congestion levels on vehicle efficiency and range, and (2) that local hauling fleets select trucks with a nominal range nearly double the expected maximum daily range to account for range losses under local, urban driving conditions.

Qiu, Yin (ORCID:0009000900948794)↗