Embedded Dense Neural Networks for Battery Cyclability Prediction on Automotive Microcontroller Devices
Posting: Harvard Library Office for Scholarly Communication, 2021
This paper explores the development of a testing methodology in the context of accurately predicting future battery performance using machine learning. In particular, I propose a Dense Neural Network Model that can predict battery life using data from the first 50 cycles in the battery life.
Recommended citation: Rotaru, A. (2021). "Embedded Dense Neural Networks for Battery Cyclability Prediction on Automotive Microcontroller Devices" Harvard Library Office for Scholarly Communication