State-of-Charge Estimation of a Li-Ion Battery using Deep Forward Neural Networks
This work addresses SOC estimation for electric vehicle batteries, but it is incremental as it applies existing deep learning methods to a specific battery model without claiming major breakthroughs.
The paper tackled State-of-Charge (SOC) estimation for a Li-ion battery by developing Deep Forward Neural Networks with two and four hidden layers, using K-fold cross-validation to model drive cycles and combat overfitting, achieving performance assessment following machine learning best practices.
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.