Open AccessReal-Time SoC Estimation for Li-Ion Batteries using Kalman Filter based on SBC Raspberry-Pi
Author(s) -
Zaini Zaini,
Dwi Mutiara Harfina,
Agung P Iswar
Publication year - 2021
Publication title -
andalas journal of electrical and electronic engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2777-0079
DOI - 10.25077/ajeeet.v1i02.12
Subject(s) - state of charge , battery (electricity) , estimator , kalman filter , matlab , extended kalman filter , automotive engineering , simulation , computer science , engineering , mathematics , statistics , power (physics) , physics , quantum mechanics , artificial intelligence , operating system
Measurement of electric charge on the battery in real-time cannot be separated from external noise and disturbances such as temperature and interference. An optimal State of Charge (SoC) estimator model is needed to make the estimation more accurate. To obtain the model, the battery was tested under room temperature conditions and at a temperature of 40oC to obtain a second-order RC model for the Li-Ion battery used. Based on the test data obtained, the data will be tested first using the Kalman Filter method to get an estimate of the State of Charge (SoC). Tests were carried out using MATLAB software. After the method was tested, the online SoC Estimator design began using the Raspberry Pi Single Board Computer (SBC). After that, the estimator will be tested first using data from offline measurements and then used in real-time (online) SoC estimation measurements. The Voc before the battery discharge test was 13.16 V and after the test, the measured Voc was 11.58 V. During the discharge the Voc was reduced by 1.58 V. While the discharge data from the battery manufacturer showed the reduced Voc during the discharge was 1.2V.