Adaptive, multi-parameter battery state estimator with optimized time-weighting factors

We derive and implement a battery control algorithm that can accommodate an arbitrary number of model parameters, with each model parameter having its own time-weighting factor, and we propose a method to determine optimal values for the time-weighting factors. Time-weighting factors are employed to...

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Veröffentlicht in:Journal of applied electrochemistry Jg. 37; H. 5; S. 605 - 616
1. Verfasser: Verbrugge, Mark
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Nature B.V 01.05.2007
Schlagworte:
Adaptive algorithms
Control algorithms
Control theory
Lithium
Lithium-ion batteries
Mathematical models
Parameters
Rechargeable batteries
State estimation
Weighting
ISSN:0021-891X, 1572-8838
Online-Zugang:Volltext
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Zusammenfassung:We derive and implement a battery control algorithm that can accommodate an arbitrary number of model parameters, with each model parameter having its own time-weighting factor, and we propose a method to determine optimal values for the time-weighting factors. Time-weighting factors are employed to give greater impact to recent data for the determination of a system’s state. We employ the (controls) methodology of weighted recursive least squares, and the time weighting corresponds to the exponential-forgetting formalism. The output from the adaptive algorithm is the battery state of charge (remaining energy), state of health (relative to the battery’s nominal performance), and predicted power capability. Results are presented for a high-power lithium ion battery.
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ISSN:0021-891X
1572-8838
DOI:10.1007/s10800-007-9291-7