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World Electr. Veh. J. 2018, 9(3), 39; https://doi.org/10.3390/wevj9030039

Battery Health Monitoring and Degradation Prognosis in Fleet Management Systems

1
Daimler AG, D-70546 Stuttgart, Germany
2
Institute of Measurement, Control, and Microtechnology, Ulm University, D-89081 Ulm, Germany
Current address: Neue Str. 95, D-73230 Kirchheim unter Teck.
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 9 July 2018 / Revised: 8 August 2018 / Accepted: 9 August 2018 / Published: 29 August 2018
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Abstract

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. View Full-Text
Keywords: lithium-ion battery; battery state of health (SoH); prediction; data acquisition; fleet monitoring lithium-ion battery; battery state of health (SoH); prediction; data acquisition; fleet monitoring
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Nuhic, A.; Bergdolt, J.; Spier, B.; Buchholz, M.; Dietmayer, K. Battery Health Monitoring and Degradation Prognosis in Fleet Management Systems. World Electr. Veh. J. 2018, 9, 39.

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