(171b) A Deep Neural Network Regression of the Cahn–Hilliard Single-Particle Thermal Model for LiFePO4 Batteries

Lithium-ion batteries serve as the primary sources of power for electric vehicles (EVs) and hybrid electric vehicles (HEVs). For vehicle applications, battery management systems (BMSs) are necessary to protect lithium-ion batteries from overheating and to ensure optimum vehicle performance. Our approach to developing a BMS was based on recent advances in the application of phase field models for lithium-ion batteries. In particular, our reduced-order model (ROM) utilized a dataset generated from the COMSOL® Multiphysics simulation of the Cahn–Hilliard equation for a single particle of a lithium iron phosphate (LiFePO4) cathode: an example of using a reduced-order model (ROM) based on a single-particle model (SPM). The main innovation of our ROM is that the SPM is fully coupled to a heat transfer model at the battery cell level. We utilized principal component analysis to identify a lower-order model that could reproduce the battery’s voltage and temperature response for ambient temperatures ranging from 253 to 298 K and for discharge rates ranging from 1 C to 20.5 C. The reduced-order dataset was then fitted to the experimental data for an A123 Systems 26650 2.3 Ah cylindrical battery using deep neural network (DNN) regression. The entire BMS is realized in conjunction with a digital-twin (DT) configuration with an offboard COMSOL® Multiphysics SPM simulation and training of the DNN, allowing the ROM to be periodically updated by retraining the DNN for aging batteries and actual operating conditions. In this configuration, only the trained DNN predictor function is onboard and in real time.