NiMH battery forensics: Instrumentation, modelling and prognostics for identifying failure

Leijen, Petrus Jozef Martinus

NiMH battery forensics: Instrumentation, modelling and prognostics for identifying failure

dc.contributor.advisor	Kularatna, Nihal
dc.contributor.advisor	Steyn-Ross, D. Alistair
dc.contributor.author	Leijen, Petrus Jozef Martinus
dc.date.accessioned	2016-09-21T04:47:20Z
dc.date.available	2016-09-21T04:47:20Z
dc.date.issued	2016
dc.date.updated	2016-09-12T21:02:11Z
dc.description.abstract	Battery forensics is a growing research field that is becoming increasingly important with the introduction of hybrid-electric and electric vehicles. The need to correctly diagnose battery condition and predict signs of early failure is well recognised. Many presently used techniques are only applicable to laboratory situations where sensitive measurement is required or where complicated mathematical approaches are needed to assess battery condition. Advanced techniques are explored, such as extended Kalman filtering, to identify the challenges associated with analysis of multi-cell battery modules. Energy-recycling hardware is developed that is capable of efficiently cycling energy to and from cells connected in a series configuration. Switching a supercapacitor-bank-based energy store between series and parallel configurations, coupled with a bidirectional switch-mode power-supply, ensures that maximum energy is retained during the analysis cycle. Extended Kalman filtering (EKF) applied to three different battery models was used to quantify the internal component values of the battery equivalent circuits. The bulk-surface model was determined to be the most appropriate for the Toyota Prius battery modules as the EKF predicted component values converge to stable values, and the recovered voltage trace has a low error. However, the computational complexity when considering 12 series-connected NiMH cells, with their individual component variation with state-of-charge and state-of-health, make the EKF approach unviable. The data harvested during the energy recycling is used to calculate a new effective capacitance measure which relates directly to battery state-of-health. Not only is there a direct relationship between effective capacitance and state-of-health, but the (Q,V) coordinate of maximum effective capacitance on the charge-voltage plane, captured during battery discharge, is able to distinguish clearly between ordinary ageing and catastrophic cell failures.
dc.format.mimetype	application/pdf
dc.identifier.citation	Leijen, P. J. M. (2016). NiMH battery forensics: Instrumentation, modelling and prognostics for identifying failure (Thesis, Doctor of Philosophy (PhD)). University of Waikato. Retrieved from https://hdl.handle.net/10289/10633	en
dc.identifier.uri	https://hdl.handle.net/10289/10633
dc.language.iso	en
dc.publisher	University of Waikato
dc.rights	All items in Research Commons are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
dc.subject	NiMH
dc.subject	battery
dc.subject	modelling
dc.subject	kalman filter
dc.subject	supercapacitor
dc.subject	switch mode power supply
dc.title	NiMH battery forensics: Instrumentation, modelling and prognostics for identifying failure
dc.type	Thesis
thesis.degree.grantor	University of Waikato
thesis.degree.level	Doctoral
thesis.degree.name	Doctor of Philosophy (PhD)