State-of-Charge Estimation of a Li-Ion Battery using Deep Forward Neural Networks

• URL: http://arxiv.org/abs/2009.09543v1
• Date: Sun, 20 Sep 2020 23:47:11 GMT
• Title: State-of-Charge Estimation of a Li-Ion Battery using Deep Forward Neural Networks
• Authors: Alexandre Barbosa de Lima and Maur\'icio B. C. Salles and Jos\'e Roberto Cardoso
• Abstract summary: We build a Deep Forward Network for a lithium-ion battery and its performance assessment. The contribution of this work is to present a methodology of building a Deep Forward Network for a lithium-ion battery and its performance assessment.
• Score: 68.8204255655161
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This article presents two Deep Forward Networks with two and four hidden layers, respectively, that model the drive cycle of a Panasonic 18650PF lithium-ion (Li-ion) battery at a given temperature using the K-fold cross-validation method, in order to estimate the State of Charge (SOC) of the cell. The drive cycle power profile is calculated for an electric truck with a 35kWh battery pack scaled for a single 18650PF cell. We propose a machine learning workflow which is able to fight overfitting when developing deep learning models for SOC estimation. The contribution of this work is to present a methodology of building a Deep Forward Network for a lithium-ion battery and its performance assessment, which follows the best practices in machine learning.

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