Matthew Guttenberg | Searcharxiv

arXiv2022

Super-linear Scaling Behavior for Electric Vehicle Chargers and Road Map to Addressing the Infrastructure Gap

Enabling widespread electric vehicle (EV) adoption requires substantial build-out of charging infrastructure in the coming decade. We formulate the charging infrastructure needs as a scaling analysis problem and use it to estimate the EV infrastructure needs of the US at a county-level resolution. Surprisingly, we find that the current EV infrastructure deployment scales super-linearly with population, deviating from the sub-linear scaling of gasoline stations and other infrastructure. We discuss how this demonstrates the infancy of EV station abundance compared to other mature transportation infrastructures. By considering the power delivery of existing gasoline stations, and appropriate EV efficiencies, we estimate the EV infrastructure gap at the county level, providing a road map for future EV infrastructure expansion. Our reliance on scaling analysis allows us to make a unique forecast in this domain.

econ.EM↗

arXiv2021

Universal Battery Performance and Degradation Model for Electric Aircraft

Development of Urban Air Mobility (UAM) concepts has been primarily focused on electric vertical takeoff and landing aircraft (eVTOLs), small aircraft which can land and takeoff vertically, and which are powered by rechargeable (typically lithium-ion) batteries. Design, analysis, and operation of eVTOLs requires fast and accurate prediction of Li-ion battery performance throughout the lifetime of the battery. eVTOL battery performance modeling must be particularly accurate at high discharge rates to ensure accurate simulation of the high power takeoff and landing portions of the flight. In this work, we generate a battery performance and thermal behavior dataset specific to eVTOL duty cycles. We use this dataset to develop a battery performance and degradation model (Cellfit) which employs physics-informed machine learning in the form of Universal Ordinary Differential Equations (U-ODE's) combined with an electrochemical cell model and degradation models which include solid electrolyte interphase (SEI) growth, lithium plating, and charge loss. We show that Cellfit with U-ODE's is better able to predict battery degradation than a mechanistic battery degradation model. We show that the improved accuracy of the degradation model improves the accuracy of the performance model. We believe that Cellfit will prove to be a valuable tool for eVTOL designers.

physics.app-ph↗