Implementation of Hardware-in-the-Loop Based Platform for Real-time Battery State of Charge Estimation on Li-Ion Batteries of Electric Vehicles using Multilayer Perceptron

Keywords: Li-Ion batteries, hardware-in-the-loop, electric vehicles, multi-layer perceptron, artificial neural networks, state of charge estimation


In this study, hardware-in-the-loop based real-time state of charge estimation was performed in Li-Ion batteries, which are widely used in hybrid and fully electric vehicles. The state of charge is estimated on the Li-Ion battery cell that forms the electric vehicle battery system. Multi-layer perceptron approach has been preferred as a method for estimating the battery state of charge. Discharge experiments under different electrical loads were applied to the Li-Ion battery cell to be used in multilayer perceptron learning processes. A special test setup has been prepared to perform the discharge process under different electrical loads. In each discharge experiment, battery open circuit voltage, battery discharge current and battery cell temperature parameters were measured and data were recorded. By using the data obtained from the experiments on the battery cell, a multi-layer perceptron was modeled in MATLAB environment. After creating the multi-layer perceptron model, the real-time charge state of the battery was estimated at different discharge currents in the experimental setup and the results obtained were evaluated.


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Author Biographies

Suleyman Ceven, Duzce University
Department of Electronics & Automation Technology, Düzce Vocational School, Düzce University, 81010- Turkey
Raif Bayir, Karabük University
Department of Mechatronics Engineering, Faculty of Technology, Karabük University, 78100- Turkey


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How to Cite
S. Ceven and R. Bayir, “Implementation of Hardware-in-the-Loop Based Platform for Real-time Battery State of Charge Estimation on Li-Ion Batteries of Electric Vehicles using Multilayer Perceptron”, IJISAE, vol. 8, no. 4, pp. 195-205, Dec. 2020.
Research Article