488 citations · 1.1k across the 11 of their papers we have counts for
14 papers
A Non-intrusive Approach for Physics-constrained Learning with Application to Fuel Cell Modeling
Vishal Srivastava, Valentin Sulzer, Peyman Mohtat +2
A data-driven model augmentation framework, referred to as Weakly-coupled Integrated Inference and Machine Learning (IIML), is presented to improve the predictive accuracy of physi…
A Continuum of Physics-Based Lithium-Ion Battery Models Reviewed
Ferran Brosa Planella, Weilong Ai, Adam M. Boyce +13
Physics-based electrochemical battery models derived from porous electrode theory are a very powerful tool for understanding lithium-ion batteries, as well as for improving their d…
Predicting the impact of formation protocols on battery lifetime immediately after manufacturing
Andrew Weng, Peyman Mohtat, Peter M. Attia +4
Increasing the speed of battery formation can significantly lower lithium-ion battery manufacturing costs. However, adopting faster formation protocols in practical manufacturing s…
"Knees" in lithium-ion battery aging trajectories
Peter M. Attia, Alexander Bills, Ferran Brosa Planella +14
Lithium-ion batteries can last many years but sometimes exhibit rapid, nonlinear degradation that severely limits battery lifetime. In this work, we review prior work on "knees" in…
Lithium-ion battery degradation: how to model it
Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula +7
Predicting lithium-ion battery degradation is worth billions to the global automotive, aviation and energy storage industries, to improve performance and safety and reduce warranty…
An Algorithmic Safety VEST For Li-ion Batteries During Fast Charging
Peyman Mohtat, Sravan Pannala, Valentin Sulzer +2
Fast charging of lithium-ion batteries is crucial to increase desirability for consumers and hence accelerate the adoption of electric vehicles. A major barrier to shorter charge t…