Earticle

초록

영어

Renewable energies use clean sources for energy generation and have the potential to balance the supply and demand of power. One of the best ways to save energy for high-demand time is to preserve it in a battery energy storage system (BESS). Various methods are presented in the last two decades for battery state of charge (SOC) estimation, however, most of them are focused only on a single battery pack and use data without accurate preprocessing and feature selection strategy. Therefore, in this paper, we conduct a comparative analysis of machine learning (ML) models with a specific preprocessing strategy and suggest a high performer model for battery rack SOC estimation. First, we preprocess the data by cleaning, normalizing, selecting important attributes, and then split it into training and testing sets. Next, four ML models are trained using the training data for SOC estimation, and finally, for better evaluation, each model is evaluated on the testing data using various error metrics. After comprehensive experiments, we suggest multilayer perceptron (MLP) due to high performance for batteries rack SOC estimation.

목차

Abstract
I. INTRODUCTION
II. LITERATURE REVIEW
III. THE PROPOSED CHARGE ESTIMATION METHOD
A. Preprocessing
B. Models Training
C. Evaluation
IV. EXPERIMENTAL RESULTS
A. Dataset
B. Results
V. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

저자

• Muhammad Munsif [ Islamia College Peshawar Peshawar, Pakistan ]
• Noman Khan [ Sejong University Seoul, Republic of Korea ]
• Tanveer Hussain [ Sejong University Seoul, Republic of Korea ]
• Muhammad Sajjad [ Islamia College Peshawar Peshawar, Pakistan ]
• Mi Young Lee [ Sejong University Seoul, Republic of Korea ]

참고문헌