Under the hood: what is BattGenie?
BattGenie provides software solutions for battery management systems (BMS). BMSes are electronic control circuits that monitor the battery’s internal states (such as State of Charge (SoC) or simply how much charge is left in the battery) and regulate the battery’s charging. If you have a rechargeable battery powering up a device or electric vehicle near you, you’ll surely find a BMS there.
What makes BattGenie unique?
BattGenie uses physics-based battery models (porous electrode P2D models) to simulate and control the battery in any application. Depending on the battery materials and usage conditions, the physics-based models account for various physical degradation phenomena such as
Li-plating, SEI layer formation, intercalation-induced stresses, and so on. Using our patented mathematical techniques, these models are used to calculate and implement optimal charge profiles, enabling batteries to be charged in under 15-min. without compromising on cycle life.
How do optimal charge profiles help?
Optimal charge profiles can extend battery cycle life and enable faster charging. These in turn have many other benefits.
Extended battery life means a greater range of the EV.
The usage time of the battery will increase, before the need to recharge it again, because of larger battery capacity.
More range/usage time means the same performance can be extracted from the battery at a reduced battery size and weight.
Reduced battery weight in EVs will make them lighter with better acceleration.
Reduced battery size and weight in consumer electronics, large building complexes, grid storage will make these solutions more economical.
Optimal charge profiles will also control battery temperature and swell more efficiently. This will lead to safer EVs, consumer electronics, and other applications.
We have demonstrated battery cycle life improvement in excess of 100%
Compared to traditional methods of battery charging through independent testing at National Renewable Energy Labs (NREL).
Engineered to be universal
Through our patented in-house numerical solvers and optimizers, we’ve developed and tested BattGenie’s algorithms on a range of microcontrollers. BattGenie’s algorithms can be solved in milliseconds on any controller.
Robust fail-safe iteration free approach for solving differential algebraic equations
The future is now, but there is more to come
The future is now, but there is more to come
CTO, Chief Scientific Advisor, and Co-Founder
Prof. Venkat Subramanian is currently the Ernest Dashiell Cockrell II Professor of Mechanical & Material Science Engineering at the University of Texas, Austin.
His research interests include energy systems engineering, electrochemical engineering, computationally efficient algorithms for state-of-charge (SOC) and state-of-health (SOH) estimation of lithium-ion batteries, multiscale simulation, and design of energetic materials, kinetic Monte Carlo methods, model-based battery management system for electric transportation, and renewable microgrids and nonlinear model predictive control. Prof. Subramanian was awarded the Dean’s award for excellence in graduate study in 2001 for his doctoral research.
He is a Fellow of the Electrochemical Society and a past Technical Editor of the Journal of the Electrochemical Society. He was also the chair of the IEEE Division of the Electrochemical Society. His codes for Lithium-ion batteries are the fastest reported in the literature and his algorithm for solving index 1 nonlinear DAEs is the most robust compared to any other algorithm reported as of today.
Prof. Subramanian received his B.Tech. degree in Chemical and Electrochemical Engineering from the Central Electrochemical Research Institute (CECRI), Karaikudi, India, in 1997 and the Ph.D. degree in Chemical Engineering from the University of South Carolina, Columbia, SC, USA, in 2001.
CEO and Co-Founder
Dr. Manan Pathak is the CEO and Co-Founder of BattGenie.
He earned his PhD from the University of Washington and he obtained his graduate thesis on model-based battery management systems. He has over 7 publications, and an extensive experience of physics-based battery models, numerical methods and deriving optimal model-based charging profiles.
CPO and Co-Founder
Dr. Chintan Pathak is the Co-Founder and CPO of BattGenie.
He earned his PhD from the University of Washington and he obtained his graduate thesis on optimal locations of battery charging stations for the state of WA. He has over 13 years of experience on software engineering and embedded systems.
Director of Business Development and Sales
Michael received an Associates Degree from Georgia Military College, studied computer science at Franklin Pierce College, completed a Bachelor’s in Business at Kennedy Western University, and completed an MBA at Warren National University, with a thesis on the Failure of Japanese Production Methodologies in Western Cultures.
Michael has a diverse background including instructor duty at the US Navy Guided Missiles School, a strong entrepreneurial spirit which led to the establishment of three commercial ventures, and he holds three product design patents.
Having worked with two of the most recognized names in the battery industry, he has significant experience leading cross-functional teams in international settings. He has been involved with the evolving landscape of Lithium batteries for the past twenty-five years and has served on standards committees such as the RTCA SC-225 and SAE International AE-7B. In the role of Business Development lead, he has served as technical advisor to many of the leading OEM and Tier One suppliers in the commercial and defense sectors of the aerospace community and has served as a subject matter expert for technical panel discussions with the FAA.
Dr. Suryanarayana Kolluri
Battery Controls Expert
Dr. Suryanarayana Kolluri is the Battery Controls Expert at BattGenie and currently working as a Research Associate at UT Austin.
He obtained his PhD from IIT Bombay and worked as a postdoctoral associate at the Maple Lab in UW Seattle. He is an expert in parameter identification and control algorithms using physics-based battery models.