State of Health Classification for Lead-acid Battery: A Data-driven Approach
In general, methods that use a data-driven approach in estimating lead-acid batteries’ State of Health (SoH) rely on measuring variables such as impedance, voltage, current, battery’s life cycle, and temperature. However, these variables only provide limited information about internal changes in the...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
|
| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/18/e3sconf_amset2024_01005.pdf |
| _version_ | 1827352530597707776 |
|---|---|
| author | Festijo Enrique Juanico Drandreb Earl Ballera Melvin Marasigan Rufo Jr. |
| author_facet | Festijo Enrique Juanico Drandreb Earl Ballera Melvin Marasigan Rufo Jr. |
| author_sort | Festijo Enrique |
| collection | DOAJ |
| description | In general, methods that use a data-driven approach in estimating lead-acid batteries’ State of Health (SoH) rely on measuring variables such as impedance, voltage, current, battery’s life cycle, and temperature. However, these variables only provide limited information about internal changes in the battery and often require sensors for accurate measurements. This study explores ultrasonic wave propagation within a lead-acid battery cell element to gather data and proposes a data-driven approach for classifying the SoH. The results demonstrate that a neural network classifier can effectively distinguish between two classes: 1) batteries in a healthy state with SoH greater than 80%, and 2) batteries in an unhealthy state with SoH less than 80%. The data-driven approach introduced in this study, which uses ultrasonic wave data, provides valuable information relative to the changes in the internal cell of the battery. Conventional external measurements may not capture this information. Consequently, it eliminates the need for additional sensor installations and offers a promising alternative for SoH classification. |
| first_indexed | 2024-03-08T03:07:14Z |
| format | Article |
| id | doaj.art-d35ecaa1cd7548e082b4cafe90e9d73c |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-08T03:07:14Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-d35ecaa1cd7548e082b4cafe90e9d73c2024-02-13T08:28:16ZengEDP SciencesE3S Web of Conferences2267-12422024-01-014880100510.1051/e3sconf/202448801005e3sconf_amset2024_01005State of Health Classification for Lead-acid Battery: A Data-driven ApproachFestijo Enrique0Juanico Drandreb Earl1Ballera Melvin2Marasigan Rufo Jr.3Gradurate Programs and Electrical Engineering Department, Technological Institute of the Philippines ManilaTechnocore CATALYST and Advanced Batteries CenterComputer Science Department, Technological Institute of the Philippines ManilaComputer Engineering Department, Technological Institute of the Philippines ManilaIn general, methods that use a data-driven approach in estimating lead-acid batteries’ State of Health (SoH) rely on measuring variables such as impedance, voltage, current, battery’s life cycle, and temperature. However, these variables only provide limited information about internal changes in the battery and often require sensors for accurate measurements. This study explores ultrasonic wave propagation within a lead-acid battery cell element to gather data and proposes a data-driven approach for classifying the SoH. The results demonstrate that a neural network classifier can effectively distinguish between two classes: 1) batteries in a healthy state with SoH greater than 80%, and 2) batteries in an unhealthy state with SoH less than 80%. The data-driven approach introduced in this study, which uses ultrasonic wave data, provides valuable information relative to the changes in the internal cell of the battery. Conventional external measurements may not capture this information. Consequently, it eliminates the need for additional sensor installations and offers a promising alternative for SoH classification.https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/18/e3sconf_amset2024_01005.pdf |
| spellingShingle | Festijo Enrique Juanico Drandreb Earl Ballera Melvin Marasigan Rufo Jr. State of Health Classification for Lead-acid Battery: A Data-driven Approach E3S Web of Conferences |
| title | State of Health Classification for Lead-acid Battery: A Data-driven Approach |
| title_full | State of Health Classification for Lead-acid Battery: A Data-driven Approach |
| title_fullStr | State of Health Classification for Lead-acid Battery: A Data-driven Approach |
| title_full_unstemmed | State of Health Classification for Lead-acid Battery: A Data-driven Approach |
| title_short | State of Health Classification for Lead-acid Battery: A Data-driven Approach |
| title_sort | state of health classification for lead acid battery a data driven approach |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/18/e3sconf_amset2024_01005.pdf |
| work_keys_str_mv | AT festijoenrique stateofhealthclassificationforleadacidbatteryadatadrivenapproach AT juanicodrandrebearl stateofhealthclassificationforleadacidbatteryadatadrivenapproach AT balleramelvin stateofhealthclassificationforleadacidbatteryadatadrivenapproach AT marasiganrufojr stateofhealthclassificationforleadacidbatteryadatadrivenapproach |