State of Power Estimation using Model Predictive Control and a Coupled Electro-Thermal Model (A123 26650)

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 MPC-based lithium-ion battery state of power estimation algorithm for the A123 26650 m1b cell developed and used in the following publications

Kawakita de Souza, A., Plett, G.L., Trimboli, M.S.,, “A Model Predictive Control-Based State of Power Estimation Algorithm Using Adaptive Weighting,” in Proc. 35nd Electric Vehicle Symposium (EVS35), Oslo, Norway, (June 2022).

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.

• This software uses Model Predictive Control(MPC) to computed the maximum discharge/charge power limit while enforcing constraints on current, voltage and temperature.
• The underlying model of this MPC-based 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.
  
• mainSOP.m is the main file to run the MPC 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.
[4] M. A. Xavier and M. S. Trimboli, “Lithium-ion battery cell-level control using constrained model predictive control and equivalent circuit models,” Journal of Power Sources, vol. 285, pp. 374–384, 2015.