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. 

Sai Thatipamula

Talk title: Electrochemical Impedance Spectroscopy for Li-ion Battery Health Estimation

Abstract: There exists a growing need for standardized On-Board Diagnostics (OBD) for electric vehicles (EVs) to provide accurate health metrics and guarantees to both consumers and manufacturers. Electrochemical Impedance Spectroscopy (EIS) is a tool that has been used to characterize and study the kinetics of several electrochemical systems. The non-invasive nature of EIS makes it an ideal candidate for evaluating the health of cells (Li-ion or otherwise) without the need for extensive teardown. Previous work has shown the powerful Li-ion Battery (LIB) capacity-based State-of-Health (SoH) estimation capability of EIS measurements and data-driven models, however such works have not sufficiently studied the explainable and powerful nature of EIS as a technique. We further this understanding by building a streamlined experimental design to optimally estimate SoH, and also use different techniques such as the Distribution of Relaxation Times (DRT) to further the accuracy and explainability of EIS-based SoH estimation strategies. The development of such optimal and explainable SoH estimation strategies has implications on EV adoption and second-life use of battery systems.

Bio: Sai Thatipamula is a 2nd year PhD student in the Energy Science and Engineering Department with Prof. Simona Onori. Sai is trained as a chemical engineer and in his first year has worked on the modeling and parameter estimation for Gasoline Particulate Filters: an important technology in the energy transition for particulate emission reduction. Using a similar skillset, he is now working on using Electrochemical Impedance Spectroscopy (EIS) as a tool for tracking and estimating the State of Health of Li-ion batteries using both data-driven and physics-based models. The talk will primarily focus on the findings so far for using EIS as a means of studying and estimating the state of health of an LIB and the future of such studies.

Mateus De Castro Ribeiro  

Talk title: 24/7 Carbon-Free Electric Fleet – Optimal Planning and Operations

Abstract: Vehicle electrification is a viable means of reducing emissions and has penetrated mobility at many levels, including heavy-duty vehicles such as bus fleets. Many regulations are accelerating electrification. An example is the Innovative Clean Transit Regulation, which mandates that all public transit agency bus fleets in California must be zero-emission by 2040. Despite these encouraging actions, fully eliminating emissions and achieving carbon neutrality requires sourcing energy for vehicles from carbon-free resources. In addition, fleet operators who have relied on diesel buses for decades face numerous barriers when transitioning to fully electric fleets, such as fueling constraints, issues with bus and electric grid interconnections, and a lack of information on incorporating and managing these new technologies. To overcome these obstacles, we present a data-driven electrification framework for achieving 24/7 carbon-free fleets. By employing machine learning and optimization tools, integrating Distributed Energy Resources (such as solar panels and battery storage), and using real data from the Stanford Marguerite Shuttle system, we aim to demonstrate that electric fleets can minimize costs and simultaneously achieve 24/7 carbon-free operations. Our scalable tool will enable fleet operators to emulate their fleets’ activities in various sustainable scenarios, accelerating and facilitating their transition to zero-emission technology.

Bio: Mateus Gheorghe de Castro Ribeiro is a second-year PhD candidate in the Stanford Sustainable Systems Lab. He has worked on various topics at the intersection of engineering applications and artificial intelligence (AI). His main area of research focuses on AI applied to sustainable energy systems, specifically using data-driven methods to accelerate the electrification of bus fleets, ensure reliable operations with minimal costs, and achieve 24/7 carbon-free operations. Mateus obtained his bachelor's and master's degrees in mechanical engineering from the Federal University of Juiz de Fora and the Pontifical Catholic University of Rio de Janeiro, respectively. In 2022, he was awarded the CAPES/Fulbright Scholarship to pursue his PhD in the Department of Civil and Environmental Engineering at Stanford University.

Tharun Reddy

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