Management and Control of Electric Vehicle Batteries

Battery Systems Engineering, Christopher D. Rahn, ISBN-13: 978-1119979500.

A Systems Approach to Lithium-Ion Battery Management, Phil Weicker, ISBN-13: 978-

Ryan Ahmed, Mohammed El Sayed, S. Andrew Gadsden, Jimi Tjong, Saeid Habibi (2016): “Artificial neural network training utilizing the smooth variable structure filter estimation strategy”, Neural Computing and Applications, Volume 27, Issue 3, pp 537–548.

Sara Rahimifard, Ryan Ahmed, Saeid Habibi (2021): “Interacting Multiple Model Strategy for Electric Vehicle Batteries State of Charge/Health/ Power Estimation”, IEEE Access Journal, Volume 9, pp 109875–109888.

Marvin Messing; Tina Shoa; Ryan Ahmed; Saeid Habibi (2020): “Battery SoC Estimation from EIS using Neural Nets”, 2020 IEEE Transportation Electrification Conference & Expo (ITEC).

G. L. Plett, "Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation," vol. 277–292, no. 134, 2004.

D. Domenico, G. Di Fiengo, and A. Stefanopoulou. Lithium-ion battery state of charge estimation with a Kalman filter based on an electrochemical model. In Proc. IEEE International Conference on Control Applications CCA, pages 702-707, Sep 2008.

I. Arasaratnam, Ryan Ahmed, M. El-Sayed, S. Habibi, and J. Tjong, (2013): Li-Ion Battery SOC Estimation Using a Bayesian Tracker, SAE World Congress, and Exhibition, Detroit, Michigan.

Carmelo Speltino, Domenico Di Domenico, Giovanni Fiengo, and Anna G. Stefanopoulou. Experimental validation of a lithium-ion battery state of charge estimation with an extended Kalman filter: Part i. In The European Control Conference, 2009.

G. L. Plett, "Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs," Journal of Power Sources, vol. 2, no. 161, p. 1369–1384, 2006.