Poster Session
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Stanford Energy Research poster presentations take place during the evening reception from 4:30pm-6:00pm on the Arrillaga Alumni Ford Fountain patio. The projects below have been selected from an application pool to represent some of the very interesting and promising novel research in energy at Stanford.
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| Accelerating sustainability: Offsetting carbon-intensive heavy-duty vehicle charging with solar and lithium-ion batteries Presenting: Joseph Lucero Co-authors: Brandon Miller, Ruixiao Sun, Vivek Sujan, Simona Onori |
| Within the US automotive sector, medium- and heavy-duty (MHD) vehicles are estimated to be responsible for 23% of annual greenhouse gas emissions, yet they only represent just under 5% of total vehicles on the road, thereby making MHD a good target for electrification. However, this additional infrastructure needed to support these vehicles will also greatly increase the electricity demand of the region. Thus, to ensure that the carbon reduction due to electrification is not mitigated by this increased electricity demand, we must supplement the grid capacity with carbon-free renewable energy resources. Leveraging a combination of Oak Ridge National Lab’s OR-SAGE and the National Renewable Energy Lab’s rEV tools to determine the availability of solar resources to power this additional infrastructure for a specific region, accounting for land availability/local bylaws, we explore how optimal dispatch of the current grid and various energy storage and conversion technologies can further enhance carbon intensity reduction. Our research thus speaks to how we can sustainably electrify MHD fleets operating around intermodal hubs in the continental USA. |
| Carbon footprint distributions of lithium-ion batteries and their materials Presenter: Leopold Peiseler Co-authors: Vanessa Schenker, Karin Schatzmann, Stephan Pfister, Tobias Schmidt |
| Our work aims to enhance climate change mitigation efforts by providing a detailed analysis of the carbon footprint (CF) associated with lithium-ion battery (LIB) production, focusing on the critical roles of material sourcing and manufacturing. We developed a novel approach by estimating emission curves for essential battery materials—lithium, nickel, and cobalt—using mining cost data, which we then integrated with global forecasts of battery cell production. This method allowed us to generate the most representative CF distributions for NMC811 and LFP cathode LIBs to date that underscore the elevated influence of material sourcing over production location on the CF. This research advances our understanding of battery CFs, providing valuable insights that can inform the development of targeted decarbonization policies and strategies within the energy sector. |
| Cold plasma for food and agriculture Presenting: Luca Vialetto Co-authors: Ken Hara |
| A novel technology for decentralized and carbon neutral fertilizer production is presented. The key idea is the use of weakly ionized gases, often called plasmas, which can be generated by renewable electricity for treatment of water. However, despite an excellent capability, comprehensive understanding of the main physical and chemical mechanisms as a function of operational parameters is still limited. In this work, we present a novel computer model that is being developed to understand the interplay between chemistry and transport for plasma-liquid interaction. As a result of the model, we assess the main chemical reaction network and the discharge properties as a function of the main operational parameters, such as applied power, material properties, and air humidity. The present results are relevant for future scaling-up and commercialization of the technology. Moreover, we highlight the potential of plasmas for decarbonization of different industrial processes. |
| Comparative analysis of numerical methods for lithium-ion battery electrochemical modeling Presenting: Le Xu Co-authors: Julian Cooper, Anirudh Allam, Simoni Onori |
| In this study, we compare two spatial discretization methods, FDM and FVM, commonly used to numerically solve the governing PDEs in the context of Lithium-ion batteries. Results show that mass in the FVM scheme is always conserved, but a small concentration drifting exist when the FDM scheme is used. This study provides findings of mass conservation analysis for FDM and FVM schemes, which we hope can facilitate BMS model selection |
| Development of circular lignin biopolymer-bound composites for construction Presenter: Barney Miao Co-authors: Robert Headrick, Zhiye Li, David Loftus, Michael Lepech |
| Currently the production of concrete accounts for 8% of global carbon emissions. Therefore, it is essential that alternatives to concrete be developed, to reduce its use in the future. New construction materials will help facilitate a green transition as envisioned in global climate initiatives. We introduce a novel material, lignin biopolymer bound soil composites (BSC), which are circular construction materials that can be implemented towards low compressive strength applications (pavers, non-load bearing walls, etc.). To develop the framework and methodology for cradle-to-cradle implementation of lignin-based BSC, a series of experimental tests were conducted, which lead to the development of a set of design relationships allowing for the design of lignin-based BSC for target compressive strength. Notably, the experimental results indicate that composites made from recycled lignin-based BSC had a noticeably higher compressive strength compared to their counterparts made from virgin materials. Additionally, a life cycle assessment (LCA) was conducted and found that lignin-based BSC were carbon negative construction materials, primarily due to the entrapment of carbon rich lignin within the BSC. Furthermore, a design guide was developed, allowing for the estimation of the life cycle carbon footprint for lignin-based BSC for a given set of design parameters. The results of this study show the promise that circular carbon negative lignin-based composites have as sustainable alternatives to concrete, and its potential role in fostering a green transition in the construction industry. |
| Domain knowledge-guided machine learning framework for SOH estimation in Lithium-ion batteries Presenting: Andrea Lanubile Co-authors: Andrea Lanubile, Pietro Bosoni, Gabriele Pozzato, Anirudh Allam, Matteo Acquarone, Simona Onori |
| In our study, we aimed to enhance electric vehicle battery management by developing an accurate method for estimating the state of health (SOH) of batteries. To achieve this, we proposed five non-cumulative health indicators derived from real-world electric vehicle data, utilizing a machine learning approach for SOH estimation. These indicators, based on power autocorrelation and energy, were validated using experimental data from electric vehicle batteries, yielding accurate online capacity estimations with an absolute percentage error as low as 1.5% to 2.5%. Our findings have significant implications for the energy sector, offering a practical, real-time solution for monitoring battery degradation, thereby improving the reliability and longevity of electric vehicle batteries. |
| Dynamic biogas control modeling for demand-driven co-generation Presenter: Jose Bolorinos Co-authors: Meagan Mauter, Ram Rajagopal |
| The goal of this work is to develop a scalable method for modeling biogas co-digestion that limits waste and increases renewable biogas electricity’s value at wastewater facilities. We use a multi-horizon forecasting and control method that combines 24-36-hour forecasts from a recurrent network trained on volumetric flows of prior digester feedstocks, and an elastic net that captures short-term boosts in biogas production from feeding of high-strength organic wastes, assumed to be controllable. We train our model on 15-minute resolution data from Silicon Valley Clean Water, which has a 1.27 MW co-generator fueled by three 6,000 m3 digesters producing roughly 12,000 m3 of biogas per day. Results show the model predicts biogas production 1-36 hours ahead with a mean absolute error equivalent to 84 kW. Our findings indicate that biogas production can be modulated for energy flexibility using commercially available sensors, which can greatly enhance its financial value and accelerate its deployment as a renewable resource. |
| E-audit: A "no-touch" energy audit that integrates machine learning and simulation Presenter: Lauren Excell Co-authors: Abigail Andrews, Rishee Jain |
| The goal of the E-Audit methodology is to improve the accessibility, cost-effectiveness, and scalability of energy audits. By using only hourly electricity data, we can identify sources of inefficiencies within a building without having to step foot inside the building. We test several machine learning methods to predict building features based on a library of physics-based building energy simulations. We found that this method shows up to 99% accuracy in predicting features with direct and indirect impacts on energy use, and that kNN balances accuracy and efficiency. |
| Effects of light-duty transport decarbonization on the Californian energy system Presenter: Mathis Heyer Co-authors: Adam Brandt |
| The goal of this work is to understand the impacts of rapid light-duty transport decarbonization on the energy system, and in particular the electricity grid, of California. For that, we develop a detailed bottom-up model of the Californian transportation sector in BRIDGES - our capacity expansion model - and compare the system impact of three policies including the zero-emission vehicle (ZEV) sales mandate, the low-carbon fuel standards, and mechanisms to reduce the vehicle miles traveled. Our results suggest that the ZEV sales mandate in its current form constitutes a suboptimal decarbonization trajectory and should be supplemented with policies such as the amended low-carbon fuel standards to achieve the state's decarbonization goals. This work may inform the ongoing amendment process of the LCFS regulation and - as our approach for the first time treats the fleet development as a variable to the optimization problem - encourage discourse on the value of scenario-based and optimization-based modeling for policy making. |
| Electric-gas infrastructure planning with focus on appliance electrification for decarbonization Presenter: Mo Sodwatana Co-authors: Dimitri Saad, Mareldi Paras, Adam Brandt |
| The goal of this work is to examine net-zero emissions target by 2045 on the energy infrastructure in California, with emphasis on how cost and technological uncertainties shape appliance electrification. We segment California into climate zones each with unique regional characteristics and utilize a coordinated gas-electric capacity expansion optimization program as the modeling tool. Results show that appliance electrification varies by pace and extent based on appliance type and region, with uncertainties encouraging or discouraging appliance electrification by up to 25%. We show that optimization-based analysis is necessary for making sound decisions around economic policy and infrastructure planning for electrification. |
| Elucidating catalyst-adsorbent interactions in dual-function materials for carbon removal and utilization Presenter: Shradha Sapru Co-authors: Kelle Hart, Bert Chandler, Arun Majumdar, Matteo Cargnello |
| Carbon capture and utilization consists of multiple challenging steps. Dual-function materials (DFMs) can reduce these cost and energy demands by coupling the capture and conversion steps in a single material and reactor as a two-step process. Understanding the multi-component interactions (Ru as the catalyst and sodium oxide as the adsorbent) in this material is important to improve their performance and stability. We synthesise pre-formed colloidal Ru nanoparticles, giving us size-controlled catalysts, and prepare libraries of samples to understand the role of each component and understand their interactions. We found that Ru plays an indirect role in increasing CO2 adsorption and that low Ru amounts worked very efficiently for this reaction. Sodium oxide determined the overall CO2 uptake capacity of the material and an effective combination of both Ru and sodium oxide was essential for best performance. These DFMs are stable over multiple cycles, with a conversion efficiency greater than 90 %. By controlling Ru-Na interactions at the molecular level, we demonstrate the critical role of metal-adsorbent interactions in this system, paving the way to design DFMs with maximum CO2 capture and conversion efficiency. |
| Impacts of relaxing humidity constraints on chilled water demand in a commercial building Presenter: Rebecca Grekin Co-authors: Jacques de Chalendar, Sally Benson |
| Here, we explore opportunities to reduce energy consumption in existing commercial buildings by explicitly considering both indoor and outdoor relative humidity (RH) in HVAC operational decision making through real-world experiments. To quantify energy savings from relaxing indoor space RH constraints, experiments were conducted in a commercial building at a corporate campus in Texas (IECC climate zone 2A, Hot Humid). Over the course of three weeks, the Air Handling Unit Supply Air Temperature (SAT) set point was repeatedly increased from their baseline of 55°F (12.8°C) or 57°F (13.9°C) to 59°F (15°C). There are three main findings from these experiments: indoor RH increased from 55% to 65%, cooling load was reduced by up to 9%, and zone-level temperatures were minimally impacted (~+0.6°F). |
| Integration of offshore wind in California Presenter: Nils Angliviel de La Beaumelle Co-authors: Inês Azevedo, Kamran Tehranchi, Matteo Simoncini, Liang Min |
| We are looking at the different aspects regarding how to integrate offshore wind in the future California electricity system. We are performing a techno-economic assessment, an expert elicitation to obtain data, a transmission and capacity expansion model, and a public acceptance study. Results are not yet final but only preliminary. We will find the costs and benefits of different transmission configurations related to the integration of offshore wind, as well as understand what is actually feasible based on public opinion. |
| Low-carbon hydrogen production via oxidant-assisted methane pyrolysis Presenter: Henry Moise Co-authors: Marco Gigantino |
| This study investigates an oxidant-assisted methane pyrolysis process, which utilizes oxidant-lean environments to enhance the net production of carbon in the process. It has been successfully demonstrated that these co-feeds drive methane conversion forward and allow for continuous carbon recovery under a fluidization regime. |
| New insights into laminar flame speeds of alternative fuels at engine-relevant high temperatures Presenter: Jackie Zheng Co-authors: Miguel Figueroa-Labastida, Ronald Hanson |
| This work deploys the novel shock-wave heating method to measure the laminar flame speeds of various alternative fuels, including hydrogen and ammonia, promising for decarbonizing the transportation and power generation sectors. Shock-wave heating provides unique access to engine-relevant thermodynamic conditions that have not been previously studied. Experimental results revealed significant shortcomings in existing chemical kinetic models used to describe the combustion of the studied fuels and provided new validation targets for the refinement of those models to aid the development of next-generation energy systems for a carbon-neutral future. |
| Observations of unstable gas-brine flows relevant to hydrogen storage Presenter: Jimin Zhou Co-author: Anthony Kovscek |
| The goal of this work is to generate experimental observations for gas-brine flow that are related to hydrogen gas storage in saline aquifers. Nitrogen and viscosified brine were validated as suitable analog fluids for hydrogen and brine, and used in a core-scale experimental setup with X-ray CT scanning. The results show that different fluid regimes provide differing saturation profiles, allowing researchers to better understand hydrogen storage in saline aquifers. |
| Onboard health estimation using distribution of relaxation rimes for lithium-ion batteries Presenter: Muhammad Aadil Khan Co-authors: Sai Thatipamula, Simona Onori |
| Real-life batteries undergo a diverse range of operating conditions that result in a combination of calendar aging and cycling aging, which makes it challenging to accurately estimate the state-of-health (SOH). Our work utilizes electrochemical impedance spectroscopy (EIS) data by converting it into distribution of relaxation times (DRT) curve using Tikhonov regularization, which contains a plethora of information about degradation mechanisms, and these curves are used as an input to a Long short-term memory (LSTM)-based neural network model. On nine different test sets, the model achieves an RMSPE of approximately 2% or less, which indicates that the model is robust. This work is impactful for onboard health estimation in order to get accurate health estimation and prolong the lifespan of lithium-ion batteries by improving their performance. |
| Predicting personalized heat stress indoors: An integrated physics and data-driven building model with wearable sensed metabolic rates Presenter: Kopal Nihar Co-authors: Rishee Jain, So-Min Cheong |
| Our work aims to enhance the prediction and understanding of extreme heat events in urban environments, focusing on the vulnerability of older adults in low-income areas, by integrating individual metabolic rates and behaviors with environmental data. We adopted an occupant-centric approach, utilizing wearable sensors, building information, and air-conditioning usage data to develop data-driven personalized heat stress models through calibrated thermal building simulations and dynamic predicted mean votes (PMV). Our findings indicate that elevated activity levels significantly influence thermal discomfort, and the integration of metabolic rates with indoor temperature data enables the prediction of extreme heat events with over 95% accuracy, outperforming traditional methods based on environmental metrics alone. This research underscores the importance of personalized data in improving heat stress predictions, with significant implications for developing early warning systems, informing building design, and enhancing indoor climate control strategies in the energy sector. |
| Reactive transport in fractured shale Presenter: Manju Pharkavi Murugesu Co-authors: Anthony Kovscek |
| The goal of this work is to understand the complex, coupled reactive transport mechanisms in fractured shale. We used a novel multi-scale and multimodal experimental technique to provide observations and further supported using reactive transport models. The results indicate that reactive transport in fractured shale leads to a self-sealing behavior of the fracture, reducing the risk of fluid leakage through caprock formations. This has significant implication when planning and monitoring CO2 storage applications in subsurface formations. |
| Selecting saline carbon storage sites in Quebec Presenter: Catherine Callas |
| The goal was to apply a site selection methodology for a direct air capture (DAC) hub scale project in the Quebec province. A multi-stage criteria driven approach was used to assess the suitability of saline reservoirs for carbon storage. The province was screened and two sites in the Gulf of St. Lawrence were identified for detailed site characterization. This work assessed the carbon storage opportunities in saline reservoirs in the Quebec province and performed an economic feasibility assessment for a hub-scale DAC project with storage at the two sites identified. |
| Spatial monitoring of CCS using time-lapse satellite images Presenter: Yunan Li Co-author: Anthony Kovscek |
| Goals: identify a standardized approach to monitor GCS projects spatially in real time. Method: we use satellite images as observational signals to interpret the subsurface events. Results: InSAR images are helpful to reduce the model uncertainties and inform GCS progress in subsurface. Impact: provide cost affordable, scalable approach to understand GCS specific sites or assets across the world for micro and macro analysis. |
| The role of energy storage in California's clean energy future Presenter: Dimitri Saad Co-authors: Mo Sodwatana, Evan Sherwin, Adam Brandt |
| 1: understanding the dynamics, the drivers, and the requirements for both electric and gas storage for decarbonization, 2: energy system optimization, multi-year modeling, 3: power-to-gas works in synergy with electric storage to reduce the required storage capacities, 4: this result highlights that the natural gas infrastructure could be retained in a decarbonized economy in California |
| Twisted epitaxy of gold nanodisc in bilayer MoS2 Presenter: Yi Cui |
| Reducing the thickness of non-layered materials such as metals to nanoscale using current layered structured two-dimensional (2D) materials as templates as well as controlling the interfacial orientation provide possibilities to synthesize novel 2D metals and heterostructures. We have recently found a "twisted epitaxial" growth method to synthesize nanometer thick gold (Au) nanodiscs by encapsulating Au nanoparticles within twisted bilayer molybdenum disulfide (MoS2) and then annealing. Transmission electron microscopy (TEM) shows that, the orientation of Au can be precisely controlled by varying the bilayer MoS2 twist angle, showing a linear and sinusoidal relationship when the bilayer twist angle is small and large, respectively. The discovery of twisted epitaxy provides opportunities for designing novel semiconductors and plasmonic devices. |
| Understanding light-driven heterogeneous catalysis from first-principles electronic structure calculations Presenter: Aaron Altman Co-authors: Emma Simmerman |
| Our work is focused on understanding the physics of photocatalysis, in which sunlight is used to drive chemical reactions, with the goal of developing engineering principles to tailor photocatalysts to specific chemical reactions. We apply high-performance computing to solve the quantum Schrodinger equation from first principles, yielding the interaction of light and electrons for realistic surfaces and nanoparticles. Our calculations show strong electronic correlations that were previously neglected in nanoparticles and qualitatively change the optical properties, and the evolution of the electronic states reveals why light-driven catalysis is can be significantly more efficient than thermal catalysis. This work forms the basis of engineering photocatalysts based on well-tabulated electronic properties of the catalyst and chemical reactants, which may help provide carbon-free approaches to e.g. the reduction of iron ore into steel, or upcycle plastics into products like synthesis gas. |
| Understanding the seal integrity for underground gas storage Presenter: Yulman Perez Claro Co-authors: Anthony Kovscek |
| 1. Understand the mass transfer mechanism in Shale. 2) X-Ray CT Scan and Image analysis. 3) The diffusion phenomena is very low compare to conventional reservoirs. 4) Implement underground carbon or energy storage in the subsurface. |
Questions about the Stanford Energy Research Showcase? Contact cindyw@stanford.edu