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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Piecewise Linear Optimization for Public EV Charging Depots in Different Utility Environments

With the proliferation of electric vehicles (EVs), EV supply equipment technology is being pushed to increasingly higher power levels, such as extreme fast charging (XFC) at 300kW or more. As more public XFC depots are connected to electric distribution systems, utility operators will mitigate their impact with pricing schemes and programs such as demand response, demand charges, and time-varying prices. In this paper we describe an XFC management system that combines an on-site battery energy storage system and an optimization application that minimizes the cost to operate the depot in light of the utility pricing programs. We have designed the optimizer specially to use fast and simple linear programming so that it can be deployed to low-cost computing platforms. We also describe how several utility pricing schemes are integrated into a single piecewise model, reducing the engineering effort to implement the optimizer in different utility environments.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Composite electrodes and methods of making the same

Disclosed herein is a composite electrode comprising a charge-conducting material, a charge-providing material bound to the charge-conducting material, and a plurality of single-walled carbon nanotubes bound to a surface of the charge-providing material. High-capacity electroactive materials that assure high performance are a prerequisite for ubiquitous adoption of technologies that require high energy/power density lithium (Li)-ion batteries, such as smart Internet of Things (IoT) devices and electric vehicles (EVs). Improved electrode performance and lifetimes are desirable. The disclosed electrode can have a Coulombic efficiency of 99% or greater, and a stable capacity retention after 100 cycles or more. Also disclosed herein are methods of making a composite electrode.

Kwon, Yo-Han↗
DOE OSTI2021

Sulfurized Polyacrylonitrile (SPAN): Changes in Mechanical Properties during Electrochemical Lithiation

Sulfurized polyacrylonitrile (SPAN) is a promising material for stable lithium-sulfur (Li-S) batteries that can potentially satisfy the demand for high-density energy storage devices for electric vehicles (EVs). However, important physical and chemical properties of the SPAN cathode material are not yet well understood. For example, the SPAN mechanical behavior that depends on the structure and chemistry of the material generated during synthesis, and the mechanical response evolution during battery discharge have been scarcely investigated. This work addresses the effects of electrochemical lithiation on the SPAN mechanical integrity via uniaxial tensile loading tests using molecular dynamics with the ReaxFF potential. Here, we evaluate the volume expansion, Young’s modulus, yield strength, and ultimate tensile strength with increasing lithium contents. Our results show how the degree of graphitization of the carbonized skeleton impacts the SPAN ability to withstand the volume expansion-induced structural stresses upon lithiation. We describe the fracture mechanisms and find out a ductility loss with increasing lithium contents

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

EVSE Cybersecurity and Resilience

Consequence-driven Cybersecurity Analysis for Extreme Fast Charging Electric Vehicle Infrastructure Electric vehicle (EV) development and associated charging infrastructure are expected to advance rapidly. Thirty percent of all global vehicle sales may be EVs and hybrid EVs by 2025, and they will rely on increasingly sophisticated strategies for grid integration. Next-generation EV charging infrastructure is expected to include interconnected renewable resources, such as photovoltaic (PV) arrays and battery storage systems, along with grid-edge devices. Although distributed energy resources (DERs) are useful in several ways, such as peak shaving at high demand times and backup supply for added resilience, the integration of vehicle charging and DERs could create more avenues for cyberattack. Physical and/or remote access to EV charging station components, including charge ports, power electronics, controllers, and local generation (e.g., PV and energy storage) could be paths to cause power fluctuations, leading to altered operations at the charging station, escalated privileges to administrative systems, exfiltration of financial information (including personally identifiable information), and reduced grid stability. One compromised EV supply equipment component can open the door to a variety of exploitable vulnerabilities. Cloud computing and mobile application control have the potential to expand the threat surface to non-repudiation and firmware integrity challenges. Vendor clouds have access to hundreds of chargers, and if compromised, can scale the attack surface exponentially. The high power and voltage levels of xFC infrastructure (e.g., 400 kW at 1000- V DC) increase the hazards and ability to impact the grid and vehicles more than lower-power charging systems. Legacy communications systems and protocols could also put EV infrastructure at risk of cyberattacks requiring a robust patch management process. Communications networks link EVs and chargers to several stakeholders - including charging station operators, grid operators, vendors/manufacturers, and aggregators - who have both physical and network access to share information for control, monitoring, and analytics. Information in these networks that is vulnerable to compromise includes the state of charge, charging duration, payment information, electricity price, and load control. Analyzing and prioritizing these interconnections risks could help address cybersecurity related to data leakage and manipulation.

charging↗
DOE OSTI2026

EVs@Scale: NextGen Profiles EVSE Characterization 2025

As part of the U.S. DOE EVs@Scale consortium, the Next-Generation Profiles (NextGen Profiles [NGP]) project presents analysis and results from the characterization of high-power conductive and wireless charging infrastructure. High Power Charging equipment is capable of recharging electric vehicle traction batteries at power levels of 200KW and above. Electric Vehicle Service Equipment (EVSE) characterization involves testing over a wide range of DC charging currents and voltages during nominal and off-nominal conditions. This testing allows for a better understanding of the impact that high-power charging will have on the electric grid. A common set of standard test plans, procedures, and data requirements were applied to the characterization in this document with minor updates and improvements. This report covers all conductive characterization activities performed between October 2024 and September 2025 on the Delta Electronics 350KW Electric Vehicle Charging System, consisting of power cabinet model EIDN-U350KTA01 and dispenser model EIDD-U350SSUUAEG-350.Key Findings include: Output regulation, Efficiency and power factor, Load management, Grid Resilience, Smart Charge Management (SCM) performance, Thermal control system performance, Multi-port simultaneous charging performance, and Selected performance comparisons with other EVSEs characterized in the NextGen Profiles project. Hot and cold temperature testing was not conducted on the Delta 350KW due to laboratory limitations. Future research could include continued testing the Delta hardware under off-nominal temperature conditions including multi-port/multi-session simultaneous charge testing, in addition to collecting data on other high-power conductive chargers to augment.

25 ENERGY STORAGE↗
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 OSTI2020

Model-Instructed Design of Novel Charging Protocols for the Extreme Fast Charging of Lithium-Ion Batteries Without Lithium Plating

Relatively long recharge time of high-energy density lithium-ion batteries (LIBs) is one of the major obstacles to the widespread deployment of electric vehicles (EVs). Excessive fast charge of LIBs can cause severe safety issues, the most significant of which is lithium plating. In addition to the consumption of cyclable lithium, dendrite-like lithium plating can cause short-circuiting within the cell, which could lead to catastrophic failure. In addition to the developments of advanced electrolytes, active materials and electrodes, the fast charging capability of LIBs can be improved by using optimized charging protocol. In this paper, the Pseudo2D battery model is first used to screen some conventional charging protocols proposed for fast charging, including the constant-current constant-voltage charging (CCCV), constant-power constant-voltage charging (CPCV), multi-stage constant-current charging, and pulse current charging. A semi-optimal charging protocol is then developed by automatically modifying the charge current such that the cell is charged at close-to-maximum current without lithium plating. Based on the features of the semi-optimal protocol, two practical novel fast charging protocols are proposed which allow charging the cell at a higher rate without lithium plating. Through simulation, we show that the no-plating capacity after 10 min charge can be improved by up to ~10.7% compared to CCCV charging for a 2.5 mAh cm -2 cell and ~18.8% for a 4 mAh cm -2 cell, when the proposed charging protocols are used.

25 ENERGY STORAGE↗
DOE OSTI2021

In–Depth Analysis of the Degradation Mechanisms of High–Nickel, Low/No–Cobalt Layered Oxide Cathodes for Lithium–Ion Batteries

A rational compositional design of high-nickel, cobalt-free layered oxide materials for high-energy and low-cost lithium-ion batteries would be expected to further propel the widespread adoption of electric vehicles (EVs), yet a composition with satisfactory electrochemical properties has yet to emerge. The previous work has demonstrated a promising LiNi 0.883 Mn 0.056 Al 0.061 O 2 (NMA-89) composition that outperformed high-nickel, cobalt-containing analogs in cycling stability and maintained a comparable rate performance and thermal stability. Herein, the capacity fading mechanism of NMA-89 in a pouch full cell with a 4.2 V cutoff is compared to that of its cobalt-containing analogs. The results reveal that particle cracking in LiNi 0.89 Mn 0.055 Co 0.055 O 2 (NMC-89) and LiNi 0.883 Co 0.053 Al 0.064 O 2 (NCA-89) leads to a loss of active material and an increase in surface area, thereby exacerbating structural and surface instabilities, accelerating impedance and polarization growth, and ultimately reducing their capacity retentions. LiNi 0.89 Mn 0.044 Co 0.042 Al 0.013 Mg 0.011 O 2 (NMCAM-89) and NMA-89 experience subdued surface reactions and maintain spherical particle structures, both of which are conducive to their capacity retentions during long-term cycling. Furthermore, this investigation offers insights into how specific transition-metal ions dictate the electrochemical stability of high-Ni layered oxide cathode materials, highlights the benefit of Mn-Al combination in NMA-89, and presents potential strategies to further enhance the performance of this novel class of cathode materials.

degradation mechanisms↗
DOE OSTI2021

The Impact of Behind-the-Meter Heterogeneous Distributed Energy Resources on Distribution Grids

The increasing integration of distributed energy resources (DERs) on the electric grid brings new challenges and opportunities for utility grid operations. With the rapid deployment of DERs, there is emerging interest in integrating these controllable devices with utility operations at all levels for monitoring and management. To understand the challenges with increasing behind-the-meter (BTM) DERs and to identify the needs in deploying advanced controls, a comprehensive grid impact study is indispensable. This paper presents the analysis which help visualize the DER impact on the grid, identify the challenges and provides an insight into the new distribution management and control needs to enable reliable and resilient grid operations.

behind-the-meter↗
DOE OSTI2026

Integrated machine learning-molecular dynamics framework for electrolyte property prediction

Electrochemical stability windows determine the operating range of battery electrolytes, yet accurate prediction remains challenging because stability emerges from statistical ensembles of local solvation environments rather than single ground-state molecular structures. Traditional density functional theory calculations on energy-minimized clusters cannot capture the thermal variations in local coordination environments and geometries that govern decomposition, while SMILES-based machine learning methods lack explicit representation of three-dimensional solvation structure and ion pairing. Here, we introduce a structure-aware machine learning framework that predicts frontier orbital energies (HOMO and LUMO) directly from molecular dynamics-sampled solvation configurations, achieving sub-0.6 eV accuracy at computational costs 3–4 orders of magnitude lower than first-principles methods. Across twelve representative battery electrolytes, we demonstrate that solvent-separated and contact ion pairs exhibit strong size- and local chemistry dependent electronic stability, with variations in coordination shifts of HOMO or LUMO level by 2–3 eV, and that extended solvation structure and partially desolvated environment further modulate stability by up to 3 eV. By encoding the statistical nature of electrochemical failure through ensemble sampling of explicit solvation geometries, our approach enables high-throughput screening and rational design of next-generation battery electrolytes with mechanistic understanding of structure–property relationships.

Energy - Storage↗
DOE OSTI2021

Challenges for and Pathways toward Li-Metal-Based All-Solid-State Batteries

Solid-state batteries utilizing Li metal anodes have the potential to enable improved performance (specific energy >500 Wh/kg, energy density >1500 Wh/L), safety, recyclability, and potentially lower cost (<$100/kWh) compared to advanced Li-ion systems.1,2 These improvements are critical for the widespread adoption of electric vehicles (EVs) and trucks and could create a short-haul electric aviation industry.1-3 Expectations for solid-state batteries are high, but there are significant materials and processing challenges to overcome.

25 ENERGY STORAGE↗
DOE OSTI2020

Effects of solvent formulations in electrolytes on fast charging of Li-ion cells

Improving the fast charging performance of lithium ion batteries (LIBs) has the promise to increase the widespread adoption of electric vehicles (EVs). Electrolyte development plays an important role in enabling fast charging. In this study, fast charging performance of LIBs is studied with different electrolytes of 1.2 M LiPF 6 in Ethylene Carbonate (EC)/Ethyl Methyl Carbonate (EMC)/co-solvents at 30/50/20 wt%. The co-solvents are methyl acetate (MA), ethyl acetate (EA), ethyl formate (EF), dimethyl carbonate (DMC) and EMC. Long term cycling performance under fast charging shows different capacity retention behaviors for different co-solvents. The structural changes in the electrode material are studied by X-ray absorption spectroscopy (XAS) and X-ray diffraction (XRD). The molarity changes in electrolyte is investigated by inductively coupled plasma-optical emission spectroscopy (ICP-OES). The electrode/electrolyte interfaces before and after fast charging are analyzed by X-ray photoemission spectroscopy (XPS). The characterization results are in good agreement with the long-term cycling performance. DMC shows the highest fast-charging capability among the five studied co-solvents due to its increased conductivity, improved electrode/electrolyte interface and stable electrode structural integrity.

25 ENERGY STORAGE↗
DOE OSTI2023

Development of a Heavy-Duty Electric Vehicle Integration and Implementation (HEVII) Tool

As demand for consumer electric vehicles (EVs) has drastically increased in recent years, manufacturers have been working to bring heavy-duty EVs to market to compete with Class 6-8 diesel-powered trucks. Many high-profile companies have committed to begin electrifying their fleet operations, but have yet to implement EVs at scale due to their limited range, long charging times, sparse charging infrastructure, and lack of data from in-use operation. Thus far, EVs have been disproportionately implemented by larger fleets with more resources. To aid fleet operators, it is imperative to develop tools to evaluate the electrification potential of heavy-duty fleets. However, commercially available tools, designed mostly for light-duty vehicles, are inadequate for making electrification recommendations tailored to a fleet of heavy-duty vehicles. The main challenge is that light-duty tools do not estimate real-time vehicle mass, a factor that has a disproportionate impact on the energy consumption of large commercial vehicles. The Heavy-Duty Electric Vehicle Integration and Implementation (HEVII) tool advances the state of the art in evaluating electrification potential and infrastructure requirements for fleets of commercial vehicles. In this work, the HEVII tool is demonstrated with non-uniformly sampled telematics data from an existing fleet to assess the suitability for electrification of each individual vehicle, determine optimal locations for charging infrastructure to support a fleet of EVs and analyze associated costs. Payload mass is predicted using sparse ground-truth data for all input drive cycles and an initial data analysis is conducted to assess the characteristics driving behaviors and energy consumption of the fleet using an adaptable vehicle model. Battery size requirements are determined by applying a novel charger placement algorithm to maximize routes that are viable for EVs and balance time delays with infrastructure development costs. This work details and demonstrates the different aspects of the HEVII tool, presenting preliminary results from an example use case.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

LiAlO 2 /LiAl 5 O 8 Membranes Derived from Flame-Synthesized Nanopowders as a Potential Electrolyte and Coating Material for All-Solid-State Batteries

Recently, γ-LiAlO 2 has attracted considerable attention as a coating in Li-ion battery electrodes. However, its potential as a Li + ceramic electrolyte is limited due to its poor ionic conductivity (<10 –10 S cm –1 ). Here, we demonstrate an effective method of processing LiAlO 2 membranes (<50 μm) using nanopowders (NPs) produced via liquid-feed flame spray pyrolysis (LF-FSP). Here, membranes consisting of selected mixtures of lithium aluminate polymorphs and Li contents were processed by conventional tape casting of NPs followed by thermocompression of the green films (100 °C/10 kpsi/10 min). The sintered green films (1100 °C/2 h/air) present a mixture of LiAlO 2 (~72 wt %) and LiAl 5 O 8 (~27 wt %) phases, offering ionic conductivities (>10 –6 S cm –1 ) at ambient with an activation energy of 0.5 eV. This greatly increases their potential utility as ceramic electrolytes for all-solid-state batteries, which could simplify battery designs, significantly reduce costs, and increase their safety. Furthermore, a solid-state Li/Li 3.1 AlO 2 /Li symmetric cell was assembled and galvanostatically cycled at 0.375 mA cm –2 current density, exhibiting a transference number ≈ 1.

25 ENERGY STORAGE↗
DOE OSTI2025

Comparative analysis of thermal management systems in electric vehicles at extreme weather conditions: Case study on Nissan Leaf 2019 Plus, Chevrolet Bolt 2020 and Tesla Model 3 2020

With the surge in electric vehicle (EV) adoption and the need for extended driving ranges, optimizing energy efficiency, particularly through thermal management, is critical, especially in extreme weather. Managing the substantial energy needed for cabin climate control and battery temperature regulation can increase energy demands by over 50 %, severely limiting range. This study conducts a comparative analysis of thermal management systems (TMS) in three popular EV vehicles, 2020 Chevrolet Bolt, 2019 Nissan Leaf Plus, and 2020 Tesla Model 3, evaluating their distinct TMS configurations and performance under varied weather conditions. Using both numerical simulations and experimental data collected on a controlled test bench at Argonne National Laboratory, we assess how TMS architecture and operational modes influence energy consumption and range. A comprehensive TMS model was developed, integrating cabin and battery thermal sub-models in the Autonomie software platform, to simulate temperature fluctuations and range impacts. Cabin climate was modeled using a mono-zonal approach, while battery cell temperature distribution was estimated through a 2D nodal structure. Each vehicle's distinct TMS setup was evaluated: the Chevrolet Bolt and Tesla Model 3 use a dual evaporator vapor compression cycle with a PTC heater for the cabin and a coolant loop for battery thermal management; the Nissan Leaf Plus employs a heat pump with a PTC heater for the cabin and air-cooling for the battery. Tests conducted at ambient temperatures of 35°C, 22°C, -7°C, and -18°C reveal significant differences in energy use and range reduction across both configurations and conditions. At 35°C, the Tesla Model 3, Chevrolet Bolt, and Nissan Leaf Plus have a range reduction of 8%, 9%, and 13%, respectively, due to air conditioning. In winter, heating technology is paramount; at -7°C, the Nissan Leaf's heat pump configuration achieves a lower range reduction (19.3%) compared to the Tesla and Chevrolet Bolt PTC heaters, which reduce range by 28.3% and 31%, respectively. Further, this study provides valuable insights for automotive engineers, EV technology researchers, and thermal management system designers aiming to enhance electric vehicle performance by understanding how different weather conditions and TMS architectures impact energy consumption and driving range.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

PyChargeModel (Oriented Programming Based Electric Vehicle and Electric Vehicle Supply Equipment Charging Model in Python) [SWR-22-39]

The PyChargeModel creates two classes called "ElectricVehicles" and "evse_class", which can be used to create multiple instances of electric vehicles (EVs) and electric vehicle supply equipment (EVSE or charging ports) and simulate charging behavior. These objects can be instantiated with several properties such as battery chemistries, battery pack sizes, cell sizes, charging port power, dc or ac chargers etc. The objects can communicate with each other by calling different methods built within the classes. Through these methods, each EV object can be assigned to an EVSE, charged either using a default protocol or using setpoint values communicated from a site controller via the EVSE object.

Mishra, Partha↗
DOE OSTI2021

A systematic solution to quantify economic values of vehicle grid integration

We report Vehicle-Grid-Integration (VGI) supplies one of the potential benefit extensions for electric vehicles (EVs) to make use of their parking time, which enables the EVs to provide grid services while still meeting consumer driving needs. However, the costs, benefits and risks of VGI still remain unclear, which limits the development of the VGI to promote the interaction between the EV and grid. In this study, we propose an integrated framework to quantify and utilize the aggregate flexibility of the EVs to supply the grid services in electricity markets. The integrated solution includes five sub-modules that cover end-to-end functionalities from individual EV energy consumption estimation to final monetary values calculation of providing grid services. Both wholesale market and local level charging management are formulated in the optimization module. A predictive control algorithm is proposed to allocate power to individual vehicles in real time, considering uncertainties from dispatch signal and travel behavior. Simulation results from 10,000 EVs indicate that the proposed optimization methods can significantly reduce the system cost in both wholesale market and retail market. Local tariff optimization reduces the electricity cost by 24.4% compared to uncontrolled charging. Wholesale market optimization results show that $\$$691 and $\$$255 revenues can be captured by each EV in ERCOT and CAISO markets per year, although with a conservative assumption on battery throughput cost at 0.16$\$$/kWh.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗