Ensemble Surrogate Models for Fast LIB Performance Predictions

Marco Quartulli, Amaia Gil, Ane Miren Florez-Tapia, Pablo Cereijo, Elixabete Ayerbe and Igor G. Olaizola
Additional contact information
Marco Quartulli: Vicomtech, Basque Research and Technology Alliance, Mikeletegi 57, 20009 Donostia-San Sebastián (ES), Spain
Amaia Gil: Vicomtech, Basque Research and Technology Alliance, Mikeletegi 57, 20009 Donostia-San Sebastián (ES), Spain
Ane Miren Florez-Tapia: Vicomtech, Basque Research and Technology Alliance, Mikeletegi 57, 20009 Donostia-San Sebastián (ES), Spain
Pablo Cereijo: CIDETEC, Basque Research and Technology Alliance, Pº Miramón 196, 20014 Donostia-San Sebastián (ES), Spain
Elixabete Ayerbe: CIDETEC, Basque Research and Technology Alliance, Pº Miramón 196, 20014 Donostia-San Sebastián (ES), Spain
Igor G. Olaizola: Vicomtech, Basque Research and Technology Alliance, Mikeletegi 57, 20009 Donostia-San Sebastián (ES), Spain

Energies, 2021, vol. 14, issue 14, 1-17

Abstract: 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.

Keywords: Li-ion battery; surrogate modeling; deep learning ensembles (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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