Module Objectives:
Introduction:
Statistical Measures:
State Space Modelling:
SS Block Diagram and Systems:
Kalman Filter Introduction:
Kalman Filter 2:
Kalman Filter 3:
Smooth Variable Structure Filter (SVSF) Concept:
SVSF Filter Operation:
Module Objectives:
By the end of this module, you should be able to:
1. Explain the fundamentals of state and parameter estimation techniques.
2. Describe how Kalman filters and Smooth Variable Structure Filters work.
3. Apply Kalman filters and Smooth Variable Structure filters to estimate the State of Charge based on the combined model in MatLab environement.
4. Linearize battery models and describe how to apply Extended Kalman filters for state extraction.
5. Describe how to formulate any mathematical model in State Space representation.
6. Determine if a given dynamic system is observable or not.
7. Describe how to formulate the OCV-R-RC Battery Model in State-Space format.
8. Tune Extended Kalman Filter to improve state estimation robustness accuracy.
9. Implement control and state estimation strategies to predict critical EV parameters such as State of Charge and State of Health.
Introduction:
Statistical Measures:
State Space Modelling:
SS Block Diagram and Systems:
Kalman Filter Introduction:
Kalman Filter 2:
Kalman Filter 3:
Smooth Variable Structure Filter (SVSF) Concept:
SVSF Filter Operation: