In this paper, a novel battery diagnosis algorithm tailored for NiMH based energy storage systems will be presented, that yields high reliability by intelligent combination of different methods including the actual accuracy of all input signals. Real time models for the faradic efficiency and the OCV hysteresis extend the feasibility of well proven methods such as charge balancing and OCV diagnosis. Moreover, a power prediction module provides the actual SOF for the motor drive in any driving situation. The underlying characteristic map is self-adapting and 'learns' changes of the battery performance due to aging and thus also allows determination of the battery state of health (SOH).
Robust algorithms for a reliable battery diagnosis - managing batteries in hybrid electric vehicles
Robuste Algorithmen für eine zuverlässige Batterie-Diagnose - Batterie-Management in elektrischen Hybridfahrzeugen
2006
12 Seiten, 10 Bilder, 6 Quellen
Aufsatz (Konferenz)
Datenträger
Englisch
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