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Images of particles made from a promising battery cathode material called NMC

Nanoimaging-inspired battery management system for electric vehicles: Translating Insights on Nanoscale Dynamics to Control Algorithms

Precourt Institute for Energy

William Chueh, Materials Science and Engineering

Student: Peter Attia, Norman Jin, Aditya Grover, Nick Perkins


Project Summary

This team involves  materials and computer scientists, who will connect the nanoscale understanding of battery degradation to the system-level control algorithms. This is a highly interdisciplinary approach that aims to improve EV battery by translating these nanoscale insights on battery degradation to advanced algorithms to manage EV battery charging and discharging. If successful, a new battery management system algorithm will be created that can be deployed to EVs within a few years.



1. Severson, K.A., Attia, P.M., Jin, N. et al. 2019. Data-driven prediction of battery cycle life before capacity degradation. Nat Energy 4, 383–391

2. Attia, P.M., Grover, A., Jin, N. et al. 2020. Closed-loop optimization of fast-charging protocols for batteries with machine learning. Nature 578, 397–402