Data-driven state-of-charge estimation of the Panasonic 18650PF Li-ion cell using deep forward neural networks

被引：1

作者：

de Lima, Alexandre B. ^{[1
]}

Salles, Mauricio B. C. ^{[1
]}

Cardoso, Jose Roberto ^{[1
]}

机构：

[1] Univ Sao Paulo, Polytech Sch, Dept Energy & Automat, Sao Paulo, Brazil

来源：

2021 14TH IEEE INTERNATIONAL CONFERENCE ON INDUSTRY APPLICATIONS (INDUSCON) | 2021年

关键词：

Deep Learning; Electrical Energy Storage; Li-ion battery; State-of-Charge; BATTERY; MODEL;

D O I：

10.1109/INDUSCON51756.2021.9529774

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The State-of-Charge (SOC) is a key parameter for the proper functioning of the Battery Management System (BMS) of lithium-ion (Li-ion) batteries, and indicates the amount of charge remaining in the battery. In this work, we present a novel empirical study for the data-driven estimation of the SOC of the Panasonic 18650PF Li-ion cell using Deep Forward Neural Networks (DFNN) and optimization algorithms with adaptive learning rates. Specifically, we model the Urban Dynamometer Driving Schedule (UDDS) drive cycle. Our results suggest that the choice of the optimization algorithm affects the performance of the model and that a DFNN with five hidden layers is the model of optimal capacity when considering 256 units per layer. This optimal DFNN is able to estimate the SOC of the 18650PF Li-ion cell with an error smaller than 0:12% over a 25 degrees C dataset using the Adamax optimization algorithm.

引用

页码：1546 / 1550

页数：5

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