RUL Estimation for EV Batteries

Estimated the Remaining Useful Life of Li-ion EV batteries with an R2 score of 98.09

A flow chart of the XGBoost Regression Model

Guides: Prof. Amit Sethi

This was a course project as part of the course DS203: Programming for Data Science taught by Prof. Amit Sethi. We chose the problem statement of predicting the remaining useful life of Li-ion EV batteries using indirect discharge cycle parameters. We started by performing exploratory data analysis on the charging, discharging and impedance cycles for Li-ion batteries using NASA’s PCoE datasets. We moved on to understanding, implementing and testing various regression models such as SVR, Multilayer Perceptron, LSTM, and various Boosting algorithms. We were able to achieve an R2 score of 98.09 for estimating the battery capacity using the XGBoost regression model.

     Report      GitHub



  1. XGBoost: A Scalable Tree Boosting System
    Tianqi Chen, and Carlos Guestrin
    In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug 2016