Machine-learning-based efficient parameter space exploration for energy storage systems
Not Available
Bayesian optimization↗
Engineering topics
Higa, Kenneth
Publications and source records attributed to Higa, Kenneth.
Zinc dendrite removal in a nickel-zinc battery with flow-through electrodes
The development and deployment of inexpensive energy storage technologies is critical to realizing a clean energy grid. Batteries are being used in this role, but there remains a need for research on systems that are designed specifically for stationary energy storage, with a focus on lowering the overall cost rather than prioritizing the system energy density, specific energy, and power output. Here, we report the development of ultra-thick (1 cm thick) electrodes with engineered flow channels and explore the variables determining how thick these electrodes can feasibly be. Our proof of concept cell, utilizing the alkaline Ni-Zn chemistry, shows stable cycling over the initial 60 cycles but still suffers from the common Zn dendrite growth at the anode. To extend the life of these systems we report our novel methodology to completely remove Zn dendrites by exploiting the flow-through nature of our electrode architecture.
Collins-Wildman, Daniel L↗
Viscosity Analysis of Battery Electrode Slurry
We report the effects of component ratios and mixing time on electrode slurry viscosity. Three component quantities were varied: active material (graphite), conductive material (carbon black), and polymer binder (carboxymethyl cellulose, CMC). The slurries demonstrated shear-thinning behavior, and suspension properties stabilized after a relatively short mixing duration. However, micrographs of the slurries suggested their internal structures did not stabilize after the same mixing time. Increasing the content of polymer binder CMC caused the greatest viscosity increase compared to that of carbon black and graphite.
25 ENERGY STORAGE↗
REST Interface Generator (RESTInG) v0.1
From a single data description file, this package generates a simple but complete RESTful web interface to a relational database, in the form of containers that run in a Docker environment. This initial version produces containers that are intended for use on the NERSC Spin platform. It was developed as a foundation for the smart catalog created for LDRD FY20 ACTS: Accelerating COVID-19 Testing with Screening.
Higa, Kenneth↗
PyGDH: Python Grid Discretization Helper
Mathematical models expressed in the form of discretized equations play an important role inmany scientific disciplines. In our experience, few domain scientists have sufficient backgroundin numerical computing (or the time required to acquire such a background) to use manyflexible and powerful but complex open source packages, such as FEniCS (Alnæs et al., 2015)and OpenFOAM (The OpenFOAM Foundation Ltd, n.d.). Many user-friendly open sourcepackages, such as FiPy (J. E. Guyer & Warren, 2009), and many commercial packages, suchas COMSOL Multiphysics (COMSOL AB, n.d.) and Simcenter STAR-CCM+ (Siemens DigitalIndustries Software, n.d.), provide limited flexibility in the equations that users can express.Additionally, the use of commercial packages, which by nature do not perform calculationstransparently, can hinder reproducibility, which is vital to the scientific process. PyGDH(“pigged”) is a Python 2 / Python 3 (Python Software Foundation, 1991–2020) package thatis meant to be accessible to scientists who might not be specialists in scientific computing,while approaching the level of flexibility associated with writing dedicated programs tailoredto solving specific problems. The PyGDH User’s Guide provides detailed instructions forcreating numerical models, including a brief introduction to necessary command line andPython (Python Software Foundation, 1991–2020) skills, and discussions of discretization andvalidation. Note that PyGDH emphasizes flexibility and simplicity over performance, and wasnot designed for high-performance applications or models describing complex spatial domains.
97 MATHEMATICS AND COMPUTING↗
LIBlib v0.1.0
LIBlib is a Python 2 / Python 3 software package (library), built on top of the PyGDH package, for relatively simple and highly flexible implementation of macrohomogenous lithium ion battery models.
Higa, Kenneth↗
The Role of Local Inhomogeneities on Dendrite Growth in LLZO-Based Solid Electrolytes
The majority of the ceramic solid electrolytes (LLZO, LATP) demonstrate polycrystalline grain/grain-boundary (G/GB) microstructure. Higher lithium (Li) concentration and lower mechanical stiffness result in current focusing at the GBs. Growth of Li dendrites through local inhomogeneities and subsequent short circuit of the cell is a major concern. Recent studies have revealed that bulk Li metal is a viscoplastic material that has low (~0.3 MPa) and high (~1.0 MPa) yield strength during deformation at smaller and larger rates of strain, respectively. It has been argued that during deposition at smaller current densities, due to its lower yield strength, Li metal should demonstrate plastic flow against stiff ceramic electrolytes, and Li dendrites will be prevented from penetrating through solid electrolytes. In this manuscript, a multiscale modeling framework has been developed for predicting properties of GBs and the bulk of ceramic electrolytes using atomistic calculations for input to mesoscale models. Using the parameters obtained from the atomistic simulations, the mesoscale model reveals that, given enough time, even at low charge rates, lithium dendrites can grow through the GBs of LLZO. The present multiscale model results also provide information regarding the dendrite growth velocity through LLZO.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗