Battery Health Monitoring and Degradation Prognosis in Fleet Management Systems
Today, fleet management systems with battery health monitoring capabilities are in the focus more than ever. This paper addresses the development of a novel battery health monitoring algorithm with a degradation prognosis feasibility particularly adapted for usage in fleet management systems. Moreov...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-08-01
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| Series: | World Electric Vehicle Journal |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2032-6653/9/3/39 |
| _version_ | 1811213447710375936 |
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| author | Adnan Nuhic Jonas Bergdolt Bernd Spier Michael Buchholz Klaus Dietmayer |
| author_facet | Adnan Nuhic Jonas Bergdolt Bernd Spier Michael Buchholz Klaus Dietmayer |
| author_sort | Adnan Nuhic |
| collection | DOAJ |
| description | Today, fleet management systems with battery health monitoring capabilities are in the focus more than ever. This paper addresses the development of a novel battery health monitoring algorithm with a degradation prognosis feasibility particularly adapted for usage in fleet management systems. Moreover, the chosen degradation prognosis approach adapts itself continuously on varying environmental conditions or utilization modes by identifying the impact factors which lead to a certain degradation trend. Such findings, when accessible with a fleet management system, offer various possibilities for fleet analysis techniques e.g., to identify an imminent battery failure. |
| first_indexed | 2024-04-12T05:46:55Z |
| format | Article |
| id | doaj.art-eb833b4592d34680a36ff6aa32d33ba0 |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-04-12T05:46:55Z |
| publishDate | 2018-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-eb833b4592d34680a36ff6aa32d33ba02022-12-22T03:45:26ZengMDPI AGWorld Electric Vehicle Journal2032-66532018-08-01933910.3390/wevj9030039wevj9030039Battery Health Monitoring and Degradation Prognosis in Fleet Management SystemsAdnan Nuhic0Jonas Bergdolt1Bernd Spier2Michael Buchholz3Klaus Dietmayer4Daimler AG, D-70546 Stuttgart, GermanyDaimler AG, D-70546 Stuttgart, GermanyDaimler AG, D-70546 Stuttgart, GermanyInstitute of Measurement, Control, and Microtechnology, Ulm University, D-89081 Ulm, GermanyInstitute of Measurement, Control, and Microtechnology, Ulm University, D-89081 Ulm, GermanyToday, fleet management systems with battery health monitoring capabilities are in the focus more than ever. This paper addresses the development of a novel battery health monitoring algorithm with a degradation prognosis feasibility particularly adapted for usage in fleet management systems. Moreover, the chosen degradation prognosis approach adapts itself continuously on varying environmental conditions or utilization modes by identifying the impact factors which lead to a certain degradation trend. Such findings, when accessible with a fleet management system, offer various possibilities for fleet analysis techniques e.g., to identify an imminent battery failure.http://www.mdpi.com/2032-6653/9/3/39lithium-ion batterybattery state of health (SoH)predictiondata acquisitionfleet monitoring |
| spellingShingle | Adnan Nuhic Jonas Bergdolt Bernd Spier Michael Buchholz Klaus Dietmayer Battery Health Monitoring and Degradation Prognosis in Fleet Management Systems World Electric Vehicle Journal lithium-ion battery battery state of health (SoH) prediction data acquisition fleet monitoring |
| title | Battery Health Monitoring and Degradation Prognosis in Fleet Management Systems |
| title_full | Battery Health Monitoring and Degradation Prognosis in Fleet Management Systems |
| title_fullStr | Battery Health Monitoring and Degradation Prognosis in Fleet Management Systems |
| title_full_unstemmed | Battery Health Monitoring and Degradation Prognosis in Fleet Management Systems |
| title_short | Battery Health Monitoring and Degradation Prognosis in Fleet Management Systems |
| title_sort | battery health monitoring and degradation prognosis in fleet management systems |
| topic | lithium-ion battery battery state of health (SoH) prediction data acquisition fleet monitoring |
| url | http://www.mdpi.com/2032-6653/9/3/39 |
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