Please use this identifier to cite or link to this item:
http://localhost:8080/xmlui/handle/123456789/3529| Title: | Identification of Li-Ion Battery Parameters Using Neural Networks |
| Authors: | Singh, Pratik Kumar Kasi, Venkata Ramana Kobaku, Tarakanath Jeyasenthil, R. Agarwal, Vivek Narlapati, Chandrasekhar Azad |
| Keywords: | Battery parameters Electric network parameters |
| Issue Date: | 2024 |
| Publisher: | Proceedings of the International Conference on Power Electronics, Drives, and Energy Systems for Industrial Growth, PEDES |
| Citation: | 10.1109/PEDES61459.2024.10961182 |
| Abstract: | There is an increase in the demand for Li-ion batteries particularly in Electric Vehicles (EVs). Identifying the Li-ion battery parameters will help in estimating the state of charge (SOC) accurately. In this paper, Li-ion battery is represented with one RC equivalent circuit. The charging/discharging behaviour of the generic battery in MATLAB-SIMULINK is used to get the relation between open circuit voltage and SOC. The circuit parameters are determined using neural network. The performance of the identified parameters is verified with the generic battery in MATLAB. |
| Description: | NITW |
| URI: | http://localhost:8080/xmlui/handle/123456789/3529 |
| Appears in Collections: | Electrical Engineering |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.