Kalman filter for adaptive learning of two-dimensional look-up tables applied to OCV-curves for aged battery cells
Journal article, 2019

In online automotive applications it is common to use look-up tables, or maps, to describe nonlinearities in component models that are to be valid over large operating ranges. If the component characteristics change with aging or wear, these look-up tables must be updated online. For 2-D look-up tables, the existing methods in the literature only adapt the observable parameters in the look-up table, which means that parameters in operation points that have not been visited for a long time may be far from their true values. In this work, correlations between different operating points are used to also update non-observable parameters of the look-up table. The method is applied to Open Circuit Voltage (OCV) curves for aged battery cells. From laboratory experimental data it is demonstrated that the proposed method can significantly reduce the average deviation from an aged OCV-curve compared to keeping the OCV-curve from the beginning of the cell's life, both for observable and non-observable parameters.

Battery estimation

2-D look-up tables

OCV-curve

Kalman filter

Batteries

Author

Anton Klintberg

Chalmers, Electrical Engineering, Systems and control, Automatic Control

Changfu Zou

Chalmers, Electrical Engineering, Systems and control, Automatic Control

B. Fridholm

Volvo Cars

Torsten Wik

Chalmers, Electrical Engineering, Systems and control, Automatic Control

Control Engineering Practice

0967-0661 (ISSN)

Vol. 84 230-237

Subject Categories

Other Physics Topics

Biomedical Laboratory Science/Technology

Other Medical Biotechnology

DOI

10.1016/j.conengprac.2018.11.023

More information

Latest update

2/11/2019