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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Analysis of Electric Vehicle Charging Behavior Patterns with Function Principal Component Analysis Approach

This manuscript focused on analyzing electric vehicles’ (EV) charging behavior patterns with a functional data analysis (FDA) approach, with the goal of providing theoretical support to the EV infrastructure planning and regulation, as well as the power grid load management. 5-year real-world charging log data from a total of 455 charging stations in Kansas City, Missouri, was used. The focuses were placed on analyzing the daily usage occupancy variability, daily energy consumption variability, and station-level usage variability. Compared with the traditional discrete-based analysis models, the proposed FDA modeling approach had unique advantages in preserving the smooth function behavior of the data, bringing more flexibility in the modeling process with little required assumptions or background knowledge on independent variables, as well as the capability of handling time series data with different lengths or sizes. In addition to the patterns revealed in the EV charging station’s occupancy and energy consumption, the differences between EV driver’s charging time and parking time were analyzed and called for the needs for parking regulation and enforcement. The different usage patterns observed at charging stations located on different land-use types were also analyzed.

Engineering↗
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 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 OSTI2024

Statistical Estimation of EV Driver Charging Behavior and Influential Factors

INL received data collected via telematics from battery electric vehicles (BEVs), and these vehicles were owned by retail customers who had entered into a telematics user agreement. The goal of analyzing these data was to develop mathematical models to characterize how different sets of BEV drivers use charging infrastructure at home and away from home (i.e., public charging) and quantify how various factors influence BEV drivers’ decision to charge and use available infrastructure. The data used in this analysis are unique because they provide real world BEV driving and charging behavior at the individual driving and parking event level. In this study we seek to leverage this data to quantify BEV charging and driving metrics to help inform models that predict quantities like the specific times when loads are imposed on the electrical grid due to BEV charging. Most models that have been developed to predict electrical grid load due to BEV charging, use simulations of BEV driving events and rely on assumptions such as every vehicle charges every night. Using a statistical modelling framework, we seek to investigate BEV charging behavior and quantitatively assess these common assumptions of BEV charging behavior.

33 - ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Effect of Si Content on Extreme Fast Charging Behavior in Silicon–Graphite Composite Anodes

Commercial Li-ion batteries typically incorporate a small amount of high-capacity silicon (Si)-based materials in the composite graphite-based anode to increase the energy density of the battery. However, very little is known about the effects of Si on the fast-charging behavior of composite anodes. Herein, we examine the effects of the Si/graphite ratio in the composite anode on the fast-charging behavior of full cells. We show that addition of Si increases the rate capability from 1C to 8C and improves the capacity retention in early cycles at 6C due to reduced overpotential in constant current charging cycles. The impacts of Si content on fast-charging aging were identified by Post-Test characterization. Despite realizing benefits of available capacity and reduced Li plating at 6C, silicon–electrolyte interactions lead the time-dependent cell performance to fade quickly in the long term. The Post-Test analysis also revealed the thickening of the electrode and nonuniform distribution of electrolyte decomposition products on the Si-containing anodes, as well as the organic-rich solid electrolyte interphase (SEI), which are the factors behind cell degradation. Our study sheds insight on the advantages and disadvantages of Si/graphite composite anodes when they are used in fast-charging applications and guides further research in the area by designing an optimized composition of Si incorporated in a mature graphite matrix.

25 ENERGY STORAGE↗
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 OSTI2021

Electric vehicle fast charging infrastructure planning in urban networks considering daily travel and charging behavior

Electric vehicles are a sustainable substitution to conventional vehicles. This work introduces an integrated framework for urban fast charging infrastructure to address the range anxiety issue. A mesoscopic simulation tool is developed to generate trip trajectories, and simulate charging behavior based on various trip attributes. The resulting charging demand is the key input to a mixed-integer nonlinear program that seeks charging station configuration. The model minimizes the total system cost including charging station and charger installation costs, and charging, queuing, and detouring delays. The problem is solved using a decomposition technique incorporating a commercial solver for small networks, and a heuristic algorithm for large-scale networks, in addition to the Golden Section method. The solution quality and significant superiority in the computational efficiency of the decomposition approach are confirmed in comparison with the implicit enumeration approach. Furthermore, the required infrastructure to support urban trips is explored for future market shares and technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Estimate long-term impact on battery degradation by considering electric vehicle real-world end-use factors

Many estimates of battery capacity degradation are based on accelerated lab tests that involve charge-discharge cycles or rely on data or electrochemical modeling. These methods are reasonable for technology benchmarking but rarely consider real-world end-use factors. To address this issue, this study develops the Battery Run-down under Electric Vehicle Operation (BREVO) model. It links the driver's travel pattern to physics-based battery degradation and powertrain energy consumption models. The model simulates the impacts of charging behavior, charging rate, driving patterns, and multiple energy management modules on battery capacity degradation. It finds that, over a 10-year timespan, firstly, for a random driver situated in the New England area, daily direct-current fast charging (60 kW) could lead to up to 22% less battery capacity when compared to daily Level-1 charging (1.8 kW). Second, the battery thermal management system can delay battery degradation by approximately 0.5% in the New England area. Third, warmer ambient temperatures enhance BEV battery usage. The model indicates that the battery capacity in the Los Angeles area is 6% higher than that in the New England area. The BREVO model provides crucial information for consumers and BEV manufacturers on range anxiety, BEV battery design, and decision support of battery warranty.

25 ENERGY STORAGE↗
DOE OSTI2024

DEPLOYING FAST CHARGING INFRASTRUCTURE FOR ELECTRIC VEHICLES IN URBAN NETWORKS: AN ACTIVITY-BASED APPROACH

This paper explores an important problem under the domain of network modeling, the optimal configuration of charging infrastructure for electric vehicles (EVs) in urban networks considering EV users' daily activities and charging behavior. This study proposes a charging behavior simulation model considering different initial state of charge (SOC), travel distance, availability of home chargers, and the daily schedule of trips for each traveler. The proposed charging behavior simulation model examines the complete chain of trips for EV users as well as the interdependency of trips traveled by each driver. The problem of finding the optimum charging configuration is then formulated as a mixed-integer nonlinear programming problem that considers the dynamics of travel time and travel distance, the interdependency of trips made by each driver, limited range of EVs, remaining battery capacity for recharging, waiting time in queue, and detour to access a charging station. This problem is solved using a metaheuristic approach for a large-scale case network. A series of examples are presented to demonstrate the model efficacy and explore the impact of energy consumption on the final SOC and the optimum charging infrastructure.

Chain of Trips↗
DOE OSTI2023

Modeling the energetic tail of a dusty plasma's electron energy distribution and its effect on dust grain charge and behavior

A model for a weakly ionized dusty plasma is proposed in which UV or x-ray radiation continuously creates free electrons at high energy, which then cool through collisions with a cold neutral gas before recombining. The transition of a free electron from high energy at birth to low energy at demise implies that the electron energy distribution is not the simple Maxwellian of an isolated system in thermal equilibrium, but instead has a high-energy tail that depends on the recombination time. This tail can have a major effect on dust grain charging because the flux of tail electrons can be substantial even if the density of tail electrons is small. Detailed analytic and numerical calculations of dust grain charging show that situations exist in which a small high-energy tail dominates charge behavior. This implies that dust grain charge in terrestrial and space dusty plasmas may be significantly underestimated if a Maxwellian distribution is assumed and the non-thermal dynamics are neglected.

Physics↗
DOE OSTI2024

Exploring impacts of electricity tariff on charging infrastructure planning: An activity-based approach

In the past decade, electric vehicles (EVs) have gained popularity for their efficiency and environmental benefits. Advances in battery technology and charging equipment have yielded long-range EVs and fast-charging. However, many major cities lack adequate charging infrastructure for daily EV use. This study addresses this gap by integrating activity-based modeling, charging behavior simulation, and charging infrastructure optimization. The research utilizes the POLARIS agent-based transportation model to accurately capture user activities, trip patterns, and traffic flows. Additionally, the study investigates the impact of fixed and spatiotemporal electricity rate distributions on optimal charging infrastructure deployment. The framework is applied to the Chicago regional area network and analyzed under various EV ownership scenarios. Further, the results reveal significant impacts of the charging pricing strategy on user decision-making and charging demand distribution. There is also a need for consistent pricing policies in charging infrastructure planning and operational phases to avoid drops in service quality.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Fast Charging Infrastructure for Electrifying Road Trips to and from National Parks in the Western United States

National parks in the Western United States draw over 80 million visitors every year, and most visitors rely on personal cars for their road trips (or long-distance travels). Travel to national parks represents distinct travel demand, as they are typically located in remote areas necessitating long-distance trips. This study investigates the quantity and locations of on-route fast charging infrastructure needed by 2030 to enable seamless travel to/from national parks using electric vehicles in seven target states in the region, employing unprecedented high-resolution spatial and temporal analysis. We find that the required number of fast charging ports for on-route charging infrastructure ranges from 1,200 to 22,000, depending on different assumptions of key input parameters - vehicle electrification rate, charging behavior, average gap between charging stations, port utilization rate, and towing trailers. Our analysis also indicates that electrical load for on-route fast charging infrastructure would peak in the afternoon, in the range of 70-400 MW, varying with the key input parameters. This study illustrates how different input parameters result in different degrees of impact on various aspects of charging infrastructure. We also examine the characteristics of projected charging infrastructure in terms of land use type, relationship with traffic volume, size of stations, and other variables.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Non-Isochronal Behavior of Charge Transport at Liquid–Liquid and Liquid–Glass Transition in Aprotic Ionic Liquids

A reversible, first-order transition separating two liquid phases of a single-component material is a fascinating yet poorly understood phenomenon. Here, we investigate the liquid–liquid transition (LLT) ability of two tetraalkylphosphonium ionic liquids (ILs), [P 666,14 ]Cl and [P 666,14 ][1,2,4-triazolide], using differential scanning calorimetry and dielectric spectroscopy. The latter technique also allowed us to study the LLT at elevated pressure. We found that cooling below 205 K transforms [P 666,14 ]Cl and [P 666,14 ][Trz] from one liquid state (liquid 1) to another (the self-assembled liquid 2), while the latter facilitates the charge transport decoupled from structural dynamics. In contrast to temperature, pressure was found to play an essential role in the self-organization of a liquid 2 phase, resulting in different time scales of charge transport for rapidly and slowly compressed samples. Furthermore, τ σ (P LL ) was found to be much shorter than τ σ (T LL , P=atm), which constitutes the first example of non-isochronal behavior of charge transport at LLT. In turn, dielectric studies through the liquid–glass transition revealed the non-monotonic behavior of τ σ at elevated pressure for [P 666,14 ]Cl, while for [P 666,14 ][Trz] τ σ (P g ) was almost constant. These results highlight the diversity of liquid–liquid transition features within the class of phosphonium ionic liquids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Phase Behavior of Charged Star Block Copolymers at Fluids Interface

The phase behavior of block copolymers (BCPs) at the water-oil interface is influenced by the segmental interaction parameter (χ) and chain architecture. We synthesized a series of star block copolymers (s-BCPs) having polystyrene (PS) as core and poly(2-vinylpyridine) (P2VP) as corona. The interaction parameters of block-block (χ PS-P2VP ) and block-solvent (χ P2VP-solvent ) were varied by adjusting the pH of the aqueous solution. Lowering pH increased the fraction of quaternized-P2VP (Q-P2VP) with enhanced hydrophilicity. By transferring the equilibrated interfacial assemblies, morphologies ranging from bicontinuous films at pH of 7 and 3.1 to nanoporous and nanotubular structure at pH of 0.65 were observed. The nanoporous films formed hexagonally packed pores in s-BCP matrix, while nanotubes comprised Q-P2VP as corona and PS as core. Control over pore size, d-spacing between pores, and nanotube diameters was achieved by varying polymer concentration, molecular weight, volume fraction and arm number of s-BCPs. Large-scale nanoporous films were obtained by freeze-drying emulsions. Remarkably, the morphologies of linear BCPs were inverted, forming hexagonal-packed rigid spherical micelles with Q-P2VP as core and PS as corona in multilayer. Here, this work provides insights of phase behaviors of BCP at fluids interface and offer a facile approach to prepare nanoporous film with well-controlled pore structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Real-Time Implementation of Smart Wireless Charging of On-Demand Shuttle Service for Demand Charge Mitigation

This paper presents a smart charge management strategy for an on-demand electric shuttle operating at the National Renewable Energy Laboratory (NREL) campus and supported by an inductive charger at the vehicle's waiting spot. A new control algorithm has been proposed for mitigating the demand charges incurred from the wireless charger. It monitors the shuttle, wireless charger, renewable energy generation, and other loads and regulates charging behavior for demand charge mitigation. Within the control algorithm, an energy prediction is made to estimate the mobility needs of the vehicle and maintain uninterrupted service during operation while still minimizing peak demand. The proposed controller is designed and optimized using a Simulink model for the entire system. It is then implemented and tested in real time at the NREL campus using online cloud services. Two vehicle-use cases' charge-sustaining and charge-depletion operation' are tested under different campus power profiles and drive cycles to assess the controller's performance. In this work, the proposed controller showed a robust performance under different driving scenarios, with high correlation between simulation and experimental data. The results show that proper demand response can be achieved, with an average of 94% reduction of charging loads during peak demand events.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Insights into the Enhanced Reversibility of Graphite Anode Upon Fast Charging Through Li Reservoir

Increasing the charging rate and reducing the charging time for Li-ion batteries are crucial to realize the mainstream of electric vehicles. However, it is formidable to avoid the Li plating on graphite anode upon fast charging. Despite the tremendous progress in Li detection techniques, the fundamental mechanism of Li plating and its chemical/electrochemical responses upon cycling still remains elusive. Herein, we present a comprehensive electrochemical method to investigate the fast charging behavior of graphite electrode. A detailed analysis is directed toward understanding the changes in phase, composition, and morphology of the fast-charged graphite. By applying a resting process, we scrutinize the further reactions of the plated Li, which readily transforms into irreversible (dead) Li. We further develop a modified graphite electrode with a thin Ag coating as the Li reservoir. The plated Li can be "absorbed" by the Ag layer to form the Li-Ag solid solution that suppresses the formation of dead Li and provides structural stability, thus promoting the further lithiation of graphite and enhancing the reversibility. Here this work not only provides additional insights into the fast charging behavior of graphite electrode but also demonstrates a potential strategy to improve the fast charging performance of graphite anode.

25 ENERGY STORAGE↗
DOE OSTI2021

Energy consumption and charging load profiles from long-haul truck electrification in the United States

Abstract The urgent need to decarbonize the transportation sector combined with falling battery prices has spurred industry and policy interest in long-haul truck electrification. The charging behavior and resulting loads from electrified long-haul freight trucks are crucial for the smooth operation of the electric grid and have far-reaching environmental impacts (e.g., greenhouse gas and other air pollutant emissions). However, the aggregate energy impact of a fleetwide shift to electrified long-haul freight trucking has not been explored. This study combines electric truck design scenarios, bottom-up truck weight modeling, vehicle energy modeling, large-scale truck traffic data, and simulation of likely operation and charging behaviors to estimate end-use energy consumption and location-specific hourly charging loads for a national fleet of long-haul electric trucks. Relative to a fleet of future diesel trucks, electrification would reduce direct end-use energy consumption by 0.9 × 10 18 J (0.9 quadrillion BTU), but electrification might increase life cycle energy consumption depending on the electricity source. The electricity required to charge long-haul electric trucks is equivalent to five percent of annual electricity consumption in the United States (US). The simulated truck charging loads peak during the day across the US grid regions, but the charging peaks’ exact timing is sensitive to when trucks are dispatched for operation. The load shapes suggest that electric trucks’ charging loads can coincide with peaks in solar power generation, and planning could enable on- or off-site integration between truck charging stations and renewable electricity generation.

Tong, Fan (ORCID:0000000346613956)↗