Battery modeling using statistical models

Project Type: Master/Diploma Thesis
Student: Maria Schag

Overview

Recently, Kalman filtering has been proposed for cell modeling in [1,2,3]. Highly promising and relevant results are shown for closely modeling the cell terminal voltage under load using the cell current as input. Furthermore, they add temperature dependence to the models and provide results where the state-of-charge (SOC) is estimated without correct initialization, i.e., the SOC is not exactly known at the beginning of operation of the cell.

The task of this thesis is to implement the models in [2,3] and to reproduce the results using data from a local company.

 

Profile of prospective student

The candidate should be interested in machine learning, applied mathematics/statistics, Matlab programming, and algorithms. Interested candidates are encouraged to ask for further information.

References

[1] Gregory L. Plett, Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 1. Background, Journal of Power Sources, Volume 134, Issue 2, 12 August 2004, Pages 252-261.

[2] Gregory L. Plett, Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification, Journal of Power Sources, Volume 134, Issue 2, 2004, Pages 262-276.

[3] Gregory L. Plett, Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation, Journal of Power Sources, Volume 134, Issue 2, 12 August 2004, Pages 277-292.