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

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 cha...