Abstract
The State of Charge (SoC) is a measurement of the amount of energy available in a battery at a specific interval of time, mostly expressed as percentage. Proportional relationships between the electromotive force of a battery, current, terminal voltage and temperature determine the SoC. There can be a considerable error in the calculations due to a sharp drop of the terminal voltage at the end of discharge. This research has explored how important SoC is, as a factor in Battery Management Systems. The work focuses on using machine learning techniques to obtain an accurate and reliable status of battery charge, this includes Random Forest, Decision Tree, Gradient Boosting, Support Vector Regression, Polynomial Regression and Multilayer Perceptron. In this paper, these techniques are tested and compared with two real world captured datasets of Lithium-ion batteries which includes LG Battery and Unibo Powertools Battery. For supporting this study, statistical methods like K-fold cross validation and Grid Search cross validation techniques are used to estimate the skill of machine learning models. After implementing these techniques, it is found that Random Forest model returns the best Accuracy and Decision Tree returns the least Mean Absolute Error.
| Original language | English |
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| Title of host publication | 2023 58th International Universities Power Engineering Conference (UPEC) |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9798350316834 |
| ISBN (Print) | 9798350316841 |
| DOIs | |
| Publication status | Published - 1 Nov 2023 |
| Event | 58th International Universities Power Engineering Conference - Technological University Dublin, Dublin, Ireland Duration: 29 Aug 2023 → 1 Sept 2023 https://upec2023.com/ (Link to conference website) |
Publication series
| Name | International Universities Power Engineering Conference (UPEC) |
|---|---|
| ISSN (Print) | 2767-9373 |
Conference
| Conference | 58th International Universities Power Engineering Conference |
|---|---|
| Abbreviated title | UPEC 2023 |
| Country/Territory | Ireland |
| City | Dublin |
| Period | 29/08/23 → 1/09/23 |
| Internet address |
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Keywords
- Battery Management Systems
- Electric Vehicles Machine Learning
- Lithium-Ion Batteries
- State of Charge (SoC)
ASJC Scopus subject areas
- Artificial Intelligence
- Energy Engineering and Power Technology
- Renewable Energy, Sustainability and the Environment
- Electrical and Electronic Engineering
- Modelling and Simulation
Documents and Links
- Author accepted manuscript
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