Kalman filter for adaptive learning of look-up tables with application to automotive battery resistance estimation
Artikel i vetenskaplig tidskrift, 2016

In online automotive applications, look-up tables are often used to model nonlinearities in component models that are to be valid over large operating ranges. If the component characteristics change with ageing or wear, these look-up tables must be updated online. Here, a method is presented where a Kalman filter is used to update the entire look-up table based on local estimation at the current operating conditions. The method is based on the idea that the parameter changes observed as a component ages are caused by physical phenomena having effect over a larger part of the operating range that may have been excited. This means that ageing patterns at different operating points are correlated, and these correlations are used to drive a random walk process that models the parameter changes. To demonstrate properties of the method, it is applied to estimate the ohmic resistance of a lithium-ion battery. In simulations the complete look-up table is successfully updated without problems of drift, even in parts of the operating range that are almost never excited. The method is also robust to uncertainties, both in the ageing model and in initial parameter estimates.


Battery resistance estimation

Parameter estimation

Kalman filter

Automotive battery

Look-up tables

Li-ion battery


B. Fridholm

IT-forskningsinstitutet Viktoria

Volvo Cars

Torsten Wik

Chalmers, Signaler och system, System- och reglerteknik, Reglerteknik

M. Nilsson

IT-forskningsinstitutet Viktoria

Control Engineering Practice

0967-0661 (ISSN)

Vol. 48 78-86





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