Ensemble Surrogate Models for Fast LIB Performance Predictions

• Published in: Energies (Basel) Vol. 14; no. 14; p. 4115
• Main Authors: Quartulli, Marco, Gil, Amaia, Florez-Tapia, Ane Miren, Cereijo, Pablo, Ayerbe, Elixabete, Olaizola, Igor G.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2021
• Subjects: Accuracy; Approximation; Control algorithms; deep learning ensembles; Design optimization; Electrodes; Electrolytes; Li-ion battery; Machine learning; Neural networks; Partial differential equations; Performance evaluation; Physics; Research methodology; Simulation; surrogate modeling
• DOI: 10.3390/en14144115
• ISSN: 1996-1073

Battery Cell design and control have been widely explored through modeling and simulation. On the one hand, Doyle’s pseudo-two-dimensional (P2D) model and Single Particle Models are among the most popular electrochemical models capable of predicting battery performance and therefore guiding cell characterization. On the other hand, empirical models obtained, for example, by Machine Learning (ML) methods represent a simpler and computationally more efficient complement to electrochemical models and have been widely used for Battery Management System (BMS) control purposes. This article proposes ML-based ensemble models to be used for the estimation of the performance of an LIB cell across a wide range of input material characteristics and parameters and evaluates 1. Deep Learning ensembles for simulation convergence classification and 2. structured regressors for battery energy and power predictions. The results represent an improvement on state-of-the-art LIB surrogate models and indicate that deep ensembles represent a promising direction for battery modeling and design.