A Physical Model-Based Observer Framework for Nonlinear Constrained State Estimation Applied to Battery State Estimation

Sensors

Jonathan Brembeck¹ 

[1] Institute of System Dynamics and Control, Robotics and Mechatronics Center, German Aerospace Center (DLR), 82234 Weßling, Germany;

关键词: nonlinear observer;    kalman filter;    constrained estimation;    state of charge estimation;    lithium-ion cell;    hybrid simulation;    functional mockup interface;    modelica;    autosar;   

DOI  :  10.3390/s19204402

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown