Paper Title
Deep Neural Network Based State of Charge Estimation on Battery Model Data and Experimental Data
Abstract
State of charge (SOC) estimation of a battery is an important task for battery management systems. Accurate
SOC estimation is required to ensure the battery’s safe and reliable operation and to prevent overcharging or undercharging,
which can lead to performance degradation and even battery failure. This paper proposes a novel method for SOC estimation
of lithium-ion batteries using Pybamm and a Deep Neural Network on experimental data and battery model dataset. The
experimental data is collected from a commercial EV battery pack, and the battery model data is generated using the
Pybamm software package. Pybamm is a Python-based battery modelling framework that can be used to simulate the
electrochemical dynamics of lithium-ion batteries. The proposed DNN model is evaluated on both the experimental data and
the battery model data, and it is shown to achieve significantly high accuracy. The proposed method is a promising new
approach for SOC estimation of lithium-ion batteries.
Keywords - Artificial Intelligence, Battery Management System, Deep Neural Networks, PyBaMM, State of Charge
Author - Dhruv Agrawal, Anirudh Yadav, Adrish Ray, Perumalla Madhusudhan
Published : Volume-10,Issue-7 ( Jul, 2023 )
DOIONLINE Number - IJAECS-IRAJ-DOIONLINE-20025
View Here
|
|
| |
|
PDF |
| |
Viewed - 17 |
| |
Published on 2023-11-15 |
|
|
|
|
|
|