Real-Time SoC Estimation for Li-Ion Batteries using Kalman Filter based on SBC Raspberry-Pi
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Electrical Engineering Department, Universitas Andalas
2021-12-01
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| Series: | Andalas Journal of Electrical and Electronic Engineering Technology |
| Subjects: | |
| Online Access: | http://ajeeet.ft.unand.ac.id/index.php/ajeeet/article/view/12 |
| _version_ | 1797769954473279488 |
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| author | Zaini Zaini Dwi Mutiara Harfina Agung P Iswar |
| author_facet | Zaini Zaini Dwi Mutiara Harfina Agung P Iswar |
| author_sort | Zaini Zaini |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T21:16:32Z |
| format | Article |
| id | doaj.art-138adc694cb8466d8940b10e9a20fc6c |
| institution | Directory Open Access Journal |
| issn | 2777-0079 |
| language | English |
| last_indexed | 2024-03-12T21:16:32Z |
| publishDate | 2021-12-01 |
| publisher | Electrical Engineering Department, Universitas Andalas |
| record_format | Article |
| series | Andalas Journal of Electrical and Electronic Engineering Technology |
| spelling | doaj.art-138adc694cb8466d8940b10e9a20fc6c2023-07-29T06:06:27ZengElectrical Engineering Department, Universitas AndalasAndalas Journal of Electrical and Electronic Engineering Technology2777-00792021-12-0112485710.25077/ajeeet.v1i2.1212Real-Time SoC Estimation for Li-Ion Batteries using Kalman Filter based on SBC Raspberry-PiZaini Zaini0Dwi Mutiara Harfina1Agung P Iswar2Universitas AndalasUniversitas AndalasUniversitas AndalasMeasurement 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.http://ajeeet.ft.unand.ac.id/index.php/ajeeet/article/view/12state of chargedischargeli-ion batterykalman filterraspberry pi |
| spellingShingle | Zaini Zaini Dwi Mutiara Harfina Agung P Iswar Real-Time SoC Estimation for Li-Ion Batteries using Kalman Filter based on SBC Raspberry-Pi Andalas Journal of Electrical and Electronic Engineering Technology state of charge discharge li-ion battery kalman filter raspberry pi |
| title | Real-Time SoC Estimation for Li-Ion Batteries using Kalman Filter based on SBC Raspberry-Pi |
| title_full | Real-Time SoC Estimation for Li-Ion Batteries using Kalman Filter based on SBC Raspberry-Pi |
| title_fullStr | Real-Time SoC Estimation for Li-Ion Batteries using Kalman Filter based on SBC Raspberry-Pi |
| title_full_unstemmed | Real-Time SoC Estimation for Li-Ion Batteries using Kalman Filter based on SBC Raspberry-Pi |
| title_short | Real-Time SoC Estimation for Li-Ion Batteries using Kalman Filter based on SBC Raspberry-Pi |
| title_sort | real time soc estimation for li ion batteries using kalman filter based on sbc raspberry pi |
| topic | state of charge discharge li-ion battery kalman filter raspberry pi |
| url | http://ajeeet.ft.unand.ac.id/index.php/ajeeet/article/view/12 |
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