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Stanford Energy Student Lectures, Week 6

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Event Details:

Please join us for the 14th Annual Stanford Student Energy Lecture Series! During the series, 16 graduate students/postdoctoral scholars, consisting of two speakers per week, will present their energy-related research to an audience of Stanford students, faculty, and staff. 


Giulio D'Acunto

Talk title: Capturing polysulfides: Crafting More Efficient Li-S Batteries with Atomic layer deposition

Abstract: Lithium-sulfur (Li-S) batteries are poised to surpass traditional lithium-ion batteries due to their higher energy densities. Despite their potential, challenges like the polysulfide shuttle effect hinder their broad adoption by affecting discharge capacity and stability. This study leverages atomic layer deposition (ALD) of Al2O3 on commercial separators, enhanced by UV ozone exposure, to mitigate these issues. We demonstrate that this method not only maintains the separator's structure but also improves its interaction with polysulfides, significantly boosting battery performance. Our ALD-enhanced separators pave the way for more efficient and stable Li-S batteries, promising for diverse applications..

Bio: Dr. Giulio D'Acunto is a Postdoctoral Researcher in the Department of Chemical Engineering at Stanford University, mentored by Prof. Stacey F. Bent and supported by the Wallenberg Foundation. Dr. D'Acunto earned his PhD in Physics from Lund University, Sweden, where he extensively utilized cutting-edge synchrotron-based techniques, including in situ and operando Ambient Pressure X-ray Photoelectron Spectroscopy (APXPS), under the guidance of Prof. Joachim Schnadt at the MAX IV Laboratory. His current research focuses on applying atomic and molecular layer deposition techniques to improve lithium metal and lithium-ion batteries. He leverages his expertise in surface characterization to explore the composition of solid-electrolyte interphases (SEIs) and surface modifications that enhance battery stability and performance.


Justin Luke 

Talk title: Jointly optimizing operations, charging infrastructure siting, and vehicle design for autonomous electric mobility-on-demand fleets

Abstract: Charging infrastructure is the coupling link between power and transportation networks, therefore careful determination of charging station siting is necessary for the effective planning of power and transportation systems. While previous works have either optimized for charging station siting given historic travel behavior, or optimized fleet routing and charging given an assumed placement of the stations, this research introduces a linear program that jointly optimizes for station siting and macroscopic fleet operations. Given an electricity rate schedule and a set of travel demand requests, the optimization minimizes the total cost for an electric autonomous mobility-on-demand fleet (E-AMoD) comprising of travel costs, station procurement costs, fleet procurement costs, and electricity costs, including demand charges. Specifically, the optimization returns the number of charging plugs for each charging rate (e.g., Level 2, various DC fast charging rates) at each candidate location, as well as the optimal routing and charging of the fleet. From a case-study of an E-AMoD fleet operating in San Francisco, our results show that, despite their range limitations, small EVs with high energy efficiencies are the most cost-effective in terms of total ownership costs. Furthermore, the optimal siting and sizing of charging stations is more spatially distributed and lower powered than the status-quo distribution of stations, consisting primarily of high-power Level 2 stations and low-power DC fast charging stations. The joint optimization reduces the total costs, empty vehicle travel, and peak charging load by up to 10% compared to only optimizing operations with status-quo distributions of charging infrastructure.

Bio: Justin Luke is a 6th-year PhD candidate in the department of Civil and Environmental Engineering and is co-advised by Ram Rajagopal and Marco Pavone. His research focuses on cost and emissions optimization of autonomous electric vehicle fleets, in particular, identifying synergies with the grid integration of renewable energy resources. In this talk, Justin will present research on novel models for the joint optimization of charging station siting and sizing, vehicle design, and fleet operations for electric autonomous mobility-on-demand (E-AMoD). In a case study of an E-AMoD fleet providing mobility services in San Francisco, the optimal siting of charging infrastructure is more spatially distributed and low-powered compared to present day infrastructure, while small, high-efficiency vehicles, despite their shorter range, are the most effective for reducing costs and emissions. Justin is supported by the Stanford Bits & Watts EV50 Project. He has obtained a MS in Electrical Engineering at Stanford in 2020 and a BS in Energy Engineering at the University of California, Berkeley in 2018.