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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Insight on electrolyte infiltration of lithium ion battery electrodes by means of a new three-dimensional-resolved lattice Boltzmann model

Electrolyte filling takes place between sealing and formation in Lithium Ion Battery (LIB) manufacturing process. This step is crucial as it is directly linked to LIB quality and affects the subsequent time consuming electrolyte wetting process. Although having fast, homogeneous and complete wetting is of paramount importance, this process has not been sufficiently examined and fully understood. For instance, experimentally available data is insufficient to fully capture the complex interplay upon filling between electrolyte and air inside the porous electrode. We report here for the first time a 3D-resolved Lattice Boltzmann Method (LBM) model able to simulate electrolyte filling upon applied pressure of LIB porous electrodes obtained both from experiments (micro X-ray tomography) and computations (stochastic generation, simulation of the manufacturing process using Coarse Grained Molecular Dynamics and Discrete Element Method). The model allows obtaining advanced insights about the impact of the electrode mesostructures on the speed of electrolyte impregnation and wetting, highlighting the importance of porosity, pore size distribution and pores interconnectivity on the filling dynamics. Furthermore, we identify scenarios where volumes with trapped air (dead zones) appear and evaluate the impact of those on the electrochemical behavior of the electrodes.

25 ENERGY STORAGE↗
DOE OSTI2022

The Importance of a Moving Boundary Approach for Modeling the SEI Layer Growth to Predict Capacity Fade

One of the contributing factors to the aging of lithium-ion batteries is the growth of the solid-electrolyte interphase (SEI) layer. The growth of the SEI layer leads to the irreversible loss of lithium available for cycling and increases the resistance of the battery. Physics-based models in literature model the kinetically limited or solvent diffusion-limited growth. In such models, the interface resistance is a constant, and the contribution to the overpotential of the intercalation reaction from the SEI layer is considered to be ohmic. In this study, we propose a model that describes the growth of the SEI layer on the electrode surface as a moving interface. The transport of lithium ions and the solvent in the electrolyte are affected by this moving interface. The equations that govern the species transport and the potential drop across the SEI layer are derived from dilute solution theory and solved by transforming the coordinates of the moving boundary. The ion transport induces changes in the conductivity across the SEI layer, which affects the potential drop that arises due to its growth. The effects of this potential on capacity fade are studied over cycling the battery.

25 ENERGY STORAGE↗
DOE OSTI2018

Formation of Multilayer Graphene Domains with Strong Sulfur-Carbon Interaction and Enhanced Sulfur Reduction Zones for Lithium-Sulfur Battery Cathodes

A newly designed sulfur/graphene computational model emulates the electrochemical behavior of a Li–S battery cathode, promoting the S–C interaction through the edges of graphene sheets. A random mixture of eight-membered sulfur rings mixed with small graphene sheets is simulated at 64 wt%sulfur loading. Structural stabilization and sulfur reduction calculations are performed with classical reactive molecular dynamics. This methodology allowed the collective behavior of the sulfur and graphene structures to be accounted for. The sulfur encapsulation induces ring opening and the sulfur phase evolves into a distribution of small chain-like structures interacting with C through the graphene edges. This new arrangement of the sulfur phase not only leads to a less pronounced volume expansion during sulfur reduction but also to a different discharge voltage profile, in qualitative agreement with earlier reports on sulfur encapsulation in microporous carbon structures. The Li 2 S phase grows around ensembles of parallel graphene nanosheets during sulfur reduction. No diffusion of sulfur or lithium between graphene nanosheets is observed, and extended Li 2 S domains bridging the space between carbon ensembles are suppressed. The results emphasize the importance of morphology on the electrochemical performance of the composite material. Finally, the sulfur/graphene model outlined here provides new understanding of the graphene effects on the sulfur reduction behavior and the role that van der Waals interactions may play in promoting formation of multilayer graphene ensembles and small Li 2 S domains during sulfur reduction.

25 ENERGY STORAGE↗
DOE OSTI2022

Data-Driven Simulation-Based Planning for Electric Airport Shuttle Systems: A Real-World Case Study

Many airports are adopting battery electric buses in their shuttle fleets due to concerns over air quality and regulations. This study proposes a simulation-based optimization modeling framework to help airport shuttle operators effectively deploy electric buses. We evaluated a planned airport electric shuttle system with an event-driven simulator. Empirical data collected from existing systems were used to drive the simulations. We then proposed a simulation-based optimization model to determine the battery capacity, charging power, and number of chargers so that predefined objective(s) (e.g., minimizing total capital cost, minimizing emissions) are optimized. Compared to existing studies, the primary contribution of the proposed method is that it can model the real-world stochastic nature of operations in an electric bus system with much higher fidelity. To demonstrate the proposed modeling framework, we study a real-world shuttle system at the Dallas-Fort Worth International Airport, and present extensive numerical studies. When considering partial fleet electrification, the model can provide a set of Pareto optimal solutions. When considering full fleet electrification, the optimal solution requires a 50-kWh battery capacity and four 210-kW chargers, resulting in a total capital cost of $26,744,000. The results demonstrate that the proposed modeling framework can effectively optimize the planning of electric airport shuttle systems with partial or full fleet electrification.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2024

Consumer-Oriented Energy Use and Range Metrics for Battery Electric Vehicles

The present study was motivated by a need to expand information for consumers offered through the FuelEconomy.Gov website. To that end, a power-based modeling approach has been used to examine the effect of steady-speed driving on estimated range for model year 2020 – 2023 battery electric vehicles (BEVs). This approach allowed rapid study of a broader range of BEV models than could be accomplished through vehicle tests. Publicly accessible certification test results and other data were used to perform a regression between cycle-average tractive power requirements and the resulting electrical power. Importantly, this regression enabled estimation of electric power and energy use over a range of steady highway speeds. These analyses in turn allowed projection of vehicle range at differing speeds. The projections agree within 6% with available 65 MPH manufacturer test data. Analyses of vehicles from model years 2020 – 2023 show that the 5-cycle range and energy use values from the window stickers of new vehicles are reasonable values for steady-speed driving at 65 MPH. Range decreases by a median value of approximately 15% for each 10 MPH increase in speed. The 5-cycle energy use (in kW-Hr / 100 miles) and energy economy (in miles / kW-Hr) derived from the 5-cycle energy use are reasonable estimates for 65 MPH steady speed driving. Energy economy decreases by about 0.5 miles / kW-Hr for every 10 MPH increase in speed.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Parametric Study of Lithium-Ion Batteries using BatPaC (Final Report)

The Battery Performance and Cost Model (BatPaC) is a tool developed by Argonne National Laboratory to design lithium-ion battery (LIB) packs for automotive applications. The material demand and design specifications are used to estimate the cost of manufacturing the battery pack in a large volume production plant (5-50 GWh/year). With the option to select from various electrode chemistries and materials with their associated properties and prices, the resultant battery pack mass, volume, and cost can vary considerably. This report is the result of a parametric sensitivity study that helps identify the key parameters and prices that affect the battery metrics – the specific energy (Wh/kg) and the pack cost ($/kWh). The study was conducted with LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC622) and graphite (G) electrodes as the baseline materials used in a battery pack for a 300-mile range battery electric vehicle (BEV300) and a 50-mile range plug-in hybrid electric vehicle (PHEV50).

25 ENERGY STORAGE↗
DOE OSTI2024

Electrolyte Design for Silicon-Based Li-Ion Battery Guided by Chemical Reactivity of Solvents with a Model Silicon Anode

Here, the use of a model compound trimethylsilyllithium was demonstrated to study the chemical reactions of electrolyte with as a principal guide to design electrolyte for silicon-based Li-ion battery. Me 3 Si - anion initiates ring-opening polymerization of EC leading to the formation of poly(ethylene ether carbonate), which subsequently defragments into oligomers and dissolves in electrolyte. FEC was found to react differently, generating LiF and vinylene carbonate (VC). Further reaction of VC with Me 3 SiLi generated poly(hydroxymethylene), which is a nonsoluble polymer and the critical SEI component. The insights from this study have guided the new electrolyte design for the Si-based battery.

25 ENERGY STORAGE↗
NASA NTRSDate not supplied

Li-ion Battery Aging with Hybrid Physics-Informed Neural Networks and Fleet-wide Data

In this work, we propose a hybrid model for Li-ion battery discharge and aging prediction that leverages fleet-wide data to predict future capacity drops.The model is built upon an hybrid approach merging physics-based and empirical equations, as well as neural network models in a recurrent neural network cell. The hybrid physics-informed neural network can predict voltage discharge cycles given the loading profile, and estimate the used capacity of the battery under random-loading conditions by tracking aging parameters connected to the residual capacity of the battery. By merging information on the battery aging parameters with existing fleet-wide aging data, the model can predict the future residual capacity of the battery that is being monitored, and therefore enable predictions of voltage discharge curves far ahead in the battery life cycle. We validated the approach using the NASA Prognostics Data Repository Battery data-set, which contains experimental data on Li-ion batteries discharged at random loading conditions in a controlled environment. The approach also allows the identification of discrepancies between the battery aging trend and the trend observed at the fleet level, so that batteries behaving differently from the rest of the fleet can be subject to closer monitoring and further testing to refine predictions.

PINN↗
DOE OSTI2022

A Comprehensive Numerical and Experimental Study for the Passive Thermal Management in Battery Modules and Packs

Cooling plates in battery packs of electric vehicles play critical roles in passive thermal management systems to reduce risks of catastrophic thermal runaway. In this work, a series of numerical simulations and experiments are carried out to unveil the role of cooling plates (both between cells and a bottom plate parallel to the cell stack) on the thermal behavior of battery modules and packs under nail penetrations. First, we investigated the role of side cooling plates on the thermal runaway propagation mitigation in battery modules (1S3P) and packs (3S3P) by varying the key parameters of the side cooling plates, such as plate thicknesses, thermal contact resistances, and materials. Then, three important factors for passive thermal management systems are identified: (i) thermal mass of side cooling plates, (ii) interfacial thermal contact resistances, and (iii) the effective heat transfer coefficients at exterior surfaces. The roles of bottom cooling plates on thermal runaway propagation mitigation in 1S3P and 1S5P battery modules are numerically investigated by comparing the thermal behavior of the modules with only side cooling plates and with both side and bottom cooling plates.

25 ENERGY STORAGE↗