The Estimation of Battery State of Charge using Corny Network
Ismail1, Firdaus2, Rakiman3, Daddy Budiman4, Sardani5
1Ismail, Department of Electrical Engineering Department, Politeknik Negeri Padang, Padang, Indonesia.
2Firdaus, Department of Electrical Engineering Department, Negeri Padang, Padang, Padang, Indonesia.
3Rakiman, Department of Mechanical Engineering, Politeknik Negeri Padang, Padang, Indonesia.
4Daddy Budiman, Department of Mechanical Engineering, Politeknik Negeri Padang, Padang, Indonesia.
5Sardani, Department of Electrical Engineering, Politeknik Negeri Padang, Padang, Indonesia.
Manuscript received on 09 January 2024 | Revised Manuscript received on 19 January 2024 | Manuscript Accepted on 15 March 2024 | Manuscript published on 30 March 2024 | PP: 5-11 | Volume-12 Issue-6, March 2024 | Retrieval Number: 100.1/ijrte.F799912060324 | DOI: 10.35940/ijrte.F7999.12060324
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: State of charge (SOC) estimation of lithium-ion batteries has been extensively studied and the estimation accuracy was mainly investigated through the development of various battery models and dynamic estimation algorithms. All battery models, however, contain inherent model bias due to the simplifications and assumptions, which cannot be effectively addressed through the development of various conventional computation and intelligent computation. Consequently, some existing methods performed battery SOC estimation using conventional and intelligent computation have not very accurate to predict the SOC battery characteristics. There some drawbacks in employment deep learning to estimate SOC battery, such as complicated algorithm or network, over fitting and so on. The proposed method, the Corny architecture has narrow layers design. This design has low cost computation and prevent over fitting. The result shows the accuracy of method is very high. The predicted and targeted values are almost merged in a single line. The RMSE and MAX error indexes are very low. That the accuracy of the model is acceptable. The electric vehicle battery can estimate to life longer and more reliable to perform mobility task. Finally, this method also show the accuracy of estimation SOC battery of electric vehicle can be solved by narrow learning layers.
Keywords: Battery, Corny Architecture, Electric Vehicle, State of Charge.
Scope of the Article: Computer Architecture and VLSI