State-of-charge Estimation of a Li-ion Battery using Deep Learning and Stochastic Optimization

2020/11/18 by Alexandre Barbosa de Lima, de Lima, Alexandre Barbosa, Maurício B. C. Salles +3
Engineering · #68T01 #Advanced Battery Technologies Research #Advancements in Battery Materials #Electric Vehicles and Infrastructure #FOS: Electrical engineering #I.2 #Signal Processing (eess.SP) #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2011.09673

openalex publication_date 2020/11/18 · openalex created_date 2020/11/23 · openalex updated_date 2026/07/28

Abstract

This article presents a novel empirical study for the estimation of the State of Charge (SOC) of a lithium-ion (Li-ion) battery which uses a deep learning model with three hidden layers. We model a series of ten vehicle drive cycles that were applied to a Panasonic 18650PF Li-ion cell. Our results show that the choice of the optimization algorithm affects the model performance. The proposed model was able to achieve an error smaller than 1.0% in all drive cycles.

Citations

• An overview of gradient descent optimization algorithms

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