Venkat Viswanathan
University of Michigan · Mechanical Engineering
Active 1975–2024
About
Venkat Viswanathan is an Associate Professor in the Department of Mechanical Engineering at the University of Michigan. His research interests include electric aviation, electric vehicles, batteries, and scientific machine learning. He holds a Ph.D. from Stanford University. Recognized for his innovative contributions, he was named an MIT Technology Review Innovator Under 35. His work focuses on advancing sustainable transportation technologies and applying machine learning techniques to energy systems.
Research topics
- Computer Science
- Materials science
- Nanotechnology
- Chemistry
- Chemical engineering
- Engineering
- Organic chemistry
- Artificial Intelligence
- Metallurgy
- Systems engineering
- Physics
- Embedded system
- Programming language
- Physical chemistry
- Thermodynamics
- Mechanical engineering
- Composite material
- Electrical engineering
- Automotive engineering
- Aerospace engineering
- Aeronautics
Selected publications
Trade-offs between automation and light vehicle electrification
Nature Energy · 51 citations
Senior authorCorresponding- Computer Science
- Automotive engineering
- Computer Science
Abstract Weight, computing load, sensor load and possibly higher drag may increase the energy use of automated electric vehicles relative to human-driven electric vehicles, although this increase may be offset by smoother driving. Here, we use a vehicle dynamics model to evaluate the trade-off between automation and electric vehicle range and battery longevity. We find that automation will likely reduce electric vehicle range by 5–10% for suburban driving and by 10–15% for city driving. The effect on range is strongly influenced by sensor drag for suburban driving and computing loads for city driving. The impact of automation on battery longevity is negligible. While some commentators have suggested that the power and energy requirements of automation mean that the first automated vehicles will be gas–electric hybrids, our results suggest that this need not be the case if automakers can implement energy-efficient computing and aerodynamic sensor stacks.
The challenges and opportunities of battery-powered flight
Nature · 2022 · 475 citations
1st authorCorresponding- Computer Science
- Aeronautics
- Engineering
AutoMat: Automated materials discovery for electrochemical systems
MRS Bulletin · 2022 · 19 citations
Senior authorCorresponding- Computer Science
- Computer Science
- Systems engineering
Applied Catalysis B Environment and Energy · 2021 · 26 citations
- Materials science
- Chemical engineering
- Nanotechnology
Advanced Materials · 2021 · 24 citations
- Materials science
- Chemical engineering
- Nanotechnology
@0.9 V iR-free is achievable in the membrane electrode assembly. Nevertheless, active catalysts with high ORR activity do not necessarily lead to high performance in the high-current-density (HCD) region. More attention shall be directed toward HCD performance for enabling high-power-density hydrogen fuel cells.
A Minimal Information Set To Enable Verifiable Theoretical Battery Research
ACS Energy Letters · 2021 · 30 citations
Senior authorCorresponding- Computer Science
- Computer Science
- Programming language
Batteries are an enabling technology for addressing sustainability through the electrification of various forms of transportation (1) and grid storage. (2) Batteries are truly multi-scale, multi-physics devices, and accordingly various theoretical descriptions exist to understand their behavior (3−5) ranging from atomistic details to techno-economic trends. As we explore advanced battery chemistries (6,7) or previously inaccessible aspects of existing ones, (8−10) new theories are required to drive decisions. (11−13) The decisions are influenced by the limitations of the underlying theory. Advanced theories used to understand battery phenomena are complicated and require substantial effort to reproduce. However, such constraints should not limit the insights from these theories. We can strive to make the theoretical research verifiable such that any battery stakeholder can assess the veracity of new theories, sophisticated simulations or elaborate analyses. We distinguish verifiability, which amounts to “Can I trust the results, conclusions and insights and identify the context where they are relevant?”, from reproducibility, which ensures “Would I get the same results if I followed the same steps?” With this motivation, we propose a checklist to guide future reports of theoretical battery research in Table 1. We hereafter discuss our thoughts leading to this and how it helps to consistently document necessary details while allowing complete freedom for creativity of individual researchers. Given the differences between experimental and theoretical studies, the proposed checklist differs from its experimental counterparts. (14,15) This checklist covers all flavors of theoretical battery research, ranging from atomic/molecular calculations (16−19) to mesoscale (20,21) and continuum-scale interactions, (9,22) and techno-economic analysis. (23,24) Also, as more and more experimental studies analyze raw data, (25) we feel this checklist would be broadly relevant.
Nature Energy · 2021 · 137 citations
- Materials science
- Chemical engineering
- Nanotechnology
Autonomous Discovery of Battery Electrolytes with Robotic Experimentation and Machine Learning
Cell Reports Physical Science · 2020 · 163 citations
Senior authorCorresponding- Computer Science
- Computer Science
- Artificial Intelligence
Innovations in batteries can require years of experimentation for design and optimization. We report an autonomous approach to the optimization of a battery electrolyte that uses machine learning coupled to a robotic test-stand to perform hundreds of sequential experiments. We search for mixtures of salts in aqueous electrolytes with high electrochemical stability using Bayesian optimization. In 40 hours of experimentation testing for 140 electrolyte formulas, we converge on a non-intuitive optimal electrolyte. The optimum is a mixed-anion sodium electrolyte that is more stable than a benchmark electrolyte, despite lower salt content, contrary to the known design principle. The precision and repeatability of the robotic test-stand distinguishes formulations that human-guided design may have missed. Our result demonstrates the possibility of integrating robotics with machine learning to discover novel battery materials. We provide a dataset characterizing 251 aqueous electrolytes containing LiNO3, LiClO4, Li2SO4, NaNO3, NaClO4, and Na2SO4 that includes conductivities, pHs, and electrochemical responses on platinum.
Challenges in Lithium Metal Anodes for Solid-State Batteries
ACS Energy Letters · 2020 · 517 citations
- Materials science
- Nanotechnology
- Chemistry
In this Perspective, we highlight recent progress and challenges related to the integration of lithium metal anodes in solid-state batteries. While prior reports have suggested that solid electrolytes may be impermeable to lithium metal, this hypothesis has been disproven under a variety of electrolyte compositions and cycling conditions. Herein, we describe the mechanistic origins and importance of lithium filament growth and interphase formation in inorganic and organic solid electrolytes. Multimodal techniques that combine real and reciprocal space imaging and modeling will be necessary to fully understand nonequilibrium dynamics at these buried interfaces. Currently, most studies on lithium electrode kinetics at solid electrolyte interfaces are completed in symmetric Li–Li configurations. To fully understand the challenges and opportunities afforded by Li-metal anodes, full-cell experiments are necessary. Finally, the impacts of operating conditions on solid-state batteries are largely unknown with respect to pressure, geometry, and break-in protocols. Given the rapid growth of this community and the diverse portfolio of solid electrolytes, we highlight the need for detailed reporting of experimental conditions and standardization of protocols across the community.
Recent grants
NSF · $210k · 2016–2020
NSF · $500k · 2016–2022
Frequent coauthors
- 55 shared
Jens K. Nørskov
Technical University of Denmark
- 35 shared
Shashank Sripad
Battery Park
- 32 shared
Dilip Krishnamurthy
Carnegie Mellon University
- 28 shared
Lance Kavalsky
University of Michigan–Ann Arbor
- 28 shared
A. C. Luntz
SLAC National Accelerator Laboratory
- 27 shared
Zeeshan Ahmad
- 27 shared
Vikram Pande
Carnegie Mellon University
- 21 shared
Heine Anton Hansen
Technical University of Denmark
Education
- 2013
Ph.D., Mechanical Engineering
Stanford University
- 2008
B. Tech., Mechanical Engineering
Indian Institute of Technology Madras
Awards & honors
- MIT Technology Review Innovators Under 35
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