Sensors, Vol. 19, Pages 4402: A Physical Model-Based Observer Framework for Nonlinear Constrained State Estimation Applied to Battery State Estimation

Sensors, Vol. 19, Pages 4402: A Physical Model-Based Observer Framework for Nonlinear Constrained State Estimation Applied to Battery State Estimation
Sensors doi: 10.3390/s19204402
Authors:
Jonathan Brembeck

Future electrified autonomous vehicles demand higly accurate knowledge of their system states to guarantee a high-fidelity and reliable control. This constitutes a challenging task—firstly, due to rising complexity and operational safeness, and secondly, due to the need for embedded service oriented architecture which demands a continuous development of new functionalities. Based on this, a novel model based Kalman filter framework is outlined in this publication, which enables the automatic incorporation of multiphysical Modelica models into discrete-time estimation algorithms. Additionally, these estimation algorithms are extended with nonlinear inequality constraint handling functionalities. The proposed framework is applied to a constrained nonlinear state of charge lithium-ion cell observer and is validated with experimental data.

Source: Sensors - October 10, 2019 Category: Biotechnology Authors: Jonathan Brembeck Tags: Article Source Type: research