  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.
