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Your search keyword '"Amifia, Lora Khaula"' showing total 13 results
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13 results on '"Amifia, Lora Khaula"'

1. Optimization of Battery Management System with SOC Estimation by Comparing Two Methods

Author

Amifia, Lora Khaula, Riansyah, Moch. Iskandar, Muttaqin, Benazir Imam Arif, Ratri, Adellia Puspita, Rifansyah, Firman Adi, Prakoso, Bagas Wahyu, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Triwiyanto, Triwiyanto, editor, Rizal, Achmad, editor, and Caesarendra, Wahyu, editor

Published
2022
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7. Model Parameter Identification of State of Charge Based on Three Battery Modelling using Kalman Filter.

Author

Amifia, Lora Khaula

Subjects
*KALMAN filtering, *OPEN-circuit voltage, *BATTERY management systems
Abstract

State of Charge (SOC) is the ratio of current versus total capacity of the battery. In the context of Battery Management System (BMS), the SOC is estimated by using a battery model. In this research, three battery models were presented, including (1) Thevenin battery model, (2) modified Thevenin battery model, and (3) simple battery model. Then, the SOC of those battery models was estimated using Coulomb Counting, Open Circuit Voltage (OCV), and Kalman Filter method. The simulation evaluated the performance of the SOC estimation methods, including the correctability of SOC initialization error. The simulation results showed that the proposed battery models could accurately estimate SOC. In terms of SOC initialization error, the Coulomb Counting, OCV Model 1, and OCV Model 2 could not correct the initialization error of SOC. However, the application of OCV Model 3 and Kalman Filter could provide an accurate SOC estimation with excellent correction of SOC initialization error. Compared to OCV model 3, the error correction in the Kalman Filter method was performed 25 minutes faster. Therefore, this finding suggests that Kalman Filter is the most suitable estimation method for BMS due to the high accuracy of SOC estimation (RMSE = 0.0014) and fast correction of SOC initialization error (time < 20 seconds). [ABSTRACT FROM AUTHOR]

Published
2022

9. Online Battery Parameter And Open Circuit Voltage (OCV) Estimation Using Recursive Least Square (RLS)

Author

Harmoko, Harmoko, Prasetyo, Dani, Widayat, Sigit Agung, Amifia, Lora Khaula, Dewangga, Bobby Rian, Cahyadi, Adha Imam, Wahyunggoro, Oyas, Harmoko, Harmoko, Prasetyo, Dani, Widayat, Sigit Agung, Amifia, Lora Khaula, Dewangga, Bobby Rian, Cahyadi, Adha Imam, and Wahyunggoro, Oyas

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%

Published
2016

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