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This summary is machine-generated.

This study introduces a novel Kalman filter framework for electrified autonomous vehicles, enabling accurate state estimation by integrating multiphysical models and handling constraints for reliable control.

Keywords:
AUTOSARconstrained estimationfunctional mockup interfacehybrid simulationkalman filterlithium-ion cellmodelicanonlinear observerstate of charge estimation

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Area of Science:

  • Automotive Engineering
  • Control Systems
  • Electrochemical Systems

Background:

  • Electrified autonomous vehicles require precise system state knowledge for reliable control.
  • Increasing vehicle complexity and the need for service-oriented architectures present estimation challenges.
  • Existing methods struggle with integrating multiphysical models and handling operational constraints.

Purpose of the Study:

  • To develop a novel model-based Kalman filter framework for enhanced state estimation in future vehicles.
  • To enable automatic incorporation of multiphysical Modelica models into estimation algorithms.
  • To extend estimation algorithms with nonlinear inequality constraint handling.

Main Methods:

  • A novel model-based Kalman filter framework was developed.
  • Multiphysical Modelica models were automatically incorporated into discrete-time estimation algorithms.
  • Nonlinear inequality constraint handling was integrated into the estimation algorithms.
  • The framework was applied to a constrained nonlinear state of charge observer for lithium-ion cells.

Main Results:

  • The proposed framework successfully integrated multiphysical models and handled nonlinear constraints.
  • The state observer demonstrated high accuracy when validated with experimental data.
  • The method provides a robust approach for state estimation in complex automotive systems.

Conclusions:

  • The developed Kalman filter framework offers a reliable solution for accurate state estimation in electrified autonomous vehicles.
  • This approach facilitates the integration of complex physical models and constraint management for improved control.
  • The validated observer performance highlights the framework's potential for embedded automotive applications.