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All Journal Mechatronics, Electrical Power, and Vehicular Technology Techne : Jurnal Ilmiah Elektroteknika
Sigit Agung Widayat
Universitas Gadjah Mada
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Comparative Study Between Internal Ohmic Resistance and Capacity for Battery State of Health Estimation M. Nisvo Ramadan; Bhisma Adji Pramana; Sigit Agung Widayat; Lora Khaula Amifia; Adha Cahyadi; Oyas Wahyunggoro
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 6, No 2 (2015)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2015.v6.113-122

Abstract

In order to avoid battery failure, a battery management system (BMS) is necessary. Battery state of charge (SOC) and state of health (SOH) are part of information provided by a BMS. This research analyzes methods to estimate SOH based lithium polymer battery on change of its internal resistance and its capacity. Recursive least square (RLS) algorithm was used to estimate internal ohmic resistance while coloumb counting was used to predict the change in the battery capacity. For the estimation algorithm, the battery terminal voltage and current are set as the input variables. Some tests including static capacity test, pulse test, pulse variation test and before charge-discharge test have been conducted to obtain the required data. After comparing the two methods, the obtained results show that SOH estimation based on coloumb counting provides better accuracy than SOH estimation based on internal ohmic resistance. However, the SOH estimation based on internal ohmic resistance is faster and more reliable for real application
Online Battery Parameter And Open Circuit Voltage (OCV) Estimation Using Recursive Least Square (RLS) Harmoko Harmoko; Dani Prasetyo; Sigit Agung Widayat; Lora Khaula Amifia; Bobby Rian Dewangga; Adha Imam Cahyadi; Oyas Wahyunggoro
Techné : Jurnal Ilmiah Elektroteknika Vol. 15 No. 01 (2016)
Publisher : Fakultas Teknik Elektronika dan Komputer Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (313.812 KB) | DOI: 10.31358/techne.v15i01.141

Abstract

After decades, the battery usage has been widespread for many applications, especially in the field of Electric Vehicle (EV). The battery is a very important component in the EV. Because the battery as the primary power source replacement of the fossil fuel. Therefore, the condition of the batteries should be always in good condition. To prevent failure of the battery for battery management system (BMS) is needed. BMS is a system to regulate the use of the battery and protects the battery from the failure of the battery supply. Many factors can be monitored at BMS, one of which is a State of Charge (SOC). SOC determination is directly related to the estimated OCV (Open Circuit Voltage). The accuracy of the estimation algorithms depend on the accuracy of the model selection to describe the dynamic characteristics of the battery. This study begins with the selection of the right model (fig.1, fig.2, fig.3) for estimating OCV. Selection of appropriate model using RLS algorithm for estimate the battery terminal voltage. Parameter that reference for determining the selection of the model is the max, min, mean, RMSE, mean RMSE of the error. Later models have been used to estimate the OCV. The result based on this research shows that modeling with n = 1 is the best result to be used in model parameter estimation and OCV battery in term of the smaller max, min, mean, rmse error. This research also show us that RLS algorithm can be estimate the parameters of the batery, OCV (fig.4), and terminal voltage of the battery with an error less than 0.1%
Co-Authors Adha Cahyadi Adha Imam Cahyadi Bhisma Adji Pramana Bobby Rian Dewangga Dani Prasetyo Harmoko Harmoko Lora Khaula Amifia M. Nisvo Ramadan Oyas Wahyunggoro