Investigation of Evolutionary Intelligence Techniques for The State of Charge Estimation of Rechargeable Batteries in Electric Vehicles (EVs) | ||||
| The International Undergraduate Research Conference | ||||
| Volume 6, Issue 6, September 2022, Page 1-10 PDF (1.1 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/iugrc.2022.302660 | ||||
 View on SCiNiTO | ||||
| Author | ||||
| Abdelwahab Ehab | ||||
| British university in Egypt, Egypt. | ||||
| Abstract | ||||
| The increase in pollution caused a shift in the industry from combustion engines toward Electrically powered vehicles. The electrification of vehicles; has an enormous impact on the research of batteries and battery management systems. The batteries go through various tests to verify their reliability and their ability to perform in a satisfactory matter, with it being the most suitable battery for the vehicle. Furthermore, the battery management system is the system responsible for overseeing various parameters, with the most important parameter being the state of charge (SOC) of the battery. SOC can be estimated using various techniques, but this paper focuses on simulating the battery using an intelligent technique, with the SOC being estimated using a neural network and compared with the SOC estimation using an extended Kalman filter. The neural network estimation method; produced valid results with high accuracy in comparison to the estimated extended Kalman filter method, and the values were closer to the real values of the SOC. | ||||
| Keywords | ||||
| State of charge (SOC); Open circuit voltage (OCV); Extended Kalman filter; Neural network | ||||
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