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A Physical Model-Based Observer Framework for Nonlinear Constrained State Estimation Applied to Battery State Estimation
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DOI：10.3390/s19204402.png)
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摘要
En 中文
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.
Keyword:
nonlinear observer
kalman filter
constrained estimation
state of charge estimation
lithium-ion cell
hybrid simulation
functional mockup interface
modelica
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期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
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