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

Institute of System Dynamics and Control, Robotics and Mechatronics Center, German Aerospace Center (DLR), 82234 Weßling, Germany
Sensors 2019, 19(20), 4402; https://doi.org/10.3390/s19204402
Received: 7 August 2019 / Revised: 30 September 2019 / Accepted: 1 October 2019 / Published: 11 October 2019
(This article belongs to the Section Physical Sensors)
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. View Full-Text
Keywords: nonlinear observer; kalman filter; constrained estimation; state of charge estimation; lithium-ion cell; hybrid simulation; functional mockup interface; modelica; AUTOSAR nonlinear observer; kalman filter; constrained estimation; state of charge estimation; lithium-ion cell; hybrid simulation; functional mockup interface; modelica; AUTOSAR
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Brembeck, J. A Physical Model-Based Observer Framework for Nonlinear Constrained State Estimation Applied to Battery State Estimation. Sensors 2019, 19, 4402.

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