SOC and Temperature Estimation Using Extended Kalman Filter and a Coupled Electro-Thermal Model (A12326650)

If you use this software in your work please cite the thesis below or reference this repository using the following DOI

This repository contains the extended Kalman filter (SPKF) algorithm for state of charge and temperature estimation for the A123 26650 m1b cell developed and used in the following publications

Kawakita de Souza, A. (2020). Advanced Predictive Control Strategies for Lithium-Ion Battery Management Using a Coupled Electro-Thermal Model [Master thesis, University of Colorado, Colorado Springs]. ProQuest Dissertations Publishing.

A. K. de Souza, G. Plett and M. S. Trimboli, "Lithium-Ion Battery Charging Control Using a Coupled Electro-Thermal Model and Model Predictive Control," 2020 IEEE Applied Power Electronics Conference and Exposition (APEC), 2020, pp. 3534-3539, doi: 10.1109/APEC39645.2020.9124431.

• The underlying model of the EKF algorithm is a coupled electro-thermal (CET) model developed in thesis above. The details of the CET model is presented below. The CET model can be parameterized using the A123 26650 dataset . For details about the model parameterization see the thesis.
  
• The EKF algorithm uses the current (ik), voltage (vk) and surface temperature (Ts,k) sensor measurements to make corrections on the SOC (zk) and temperature (Tc,k) estimates. For details about the implementation of the EKF algorithm see the thesis.
• mainEKF.m is the main file to run the EKF algorithm

References:
[1] G. L. Plett, Battery Management Systems, Volume 1: Battery Modeling. Artech House, 2015.
[2] X. Lin, H. E. Perez, S. Mohan, J. B. Siegel, A. G. Stefanopoulou,Y. Ding, and M. P. Castanier, “A lumped-parameter electro-thermal model for cylindrical batteries,”Journal of PowerSources, vol. 257, pp. 1–11, Jul. 2014.
[3] G. L. Plett, Battery Management Systems, Volume 2: Equivalent-Circuit Methods. Artech House, 2015.