Kalman filter design for estimation and filtering of main parameters in a marine hybrid diesel-electric propulsion powertrain - Chantzaras, Alexandros (Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ναυπηγών Μηχανολόγων Μηχανικών. Τομέας Ναυτικής Μηχανολογίας. Εργαστήριο Ναυτικής Μηχανολογίας, 2017)
In this thesis, the feasibility of using state space model design with Kalman Filter for the estimation of engine responses and exhaust emissions, using as inputs parameters like engine speed, manifold air pressure and lambda values, has been investigated. The modelling procedure is divided into three parts. The aim of first part is to find linear models using datasets with experimental data from a marine diesel propulsion engine at the hybrid diesel-electric testbed at LME (HIPPO-1). Second part aims at the configuration of the linear multi-variable models which can describe the engine behaviour, through a feedback manipulation, so that the models are improved in terms of overshooting and damping characteristics. Finally, the implementation of Kalman Filter is introduced, so as to reduce noise effects, which corrupt the signals in real-life applications. All the models obtained, manipulated or not, have been validated with data from two sets of experiments: (a) alternating load (torque), representing a generator on-board a ship with constant speed, and (b) in propeller loading operation, with alternating speed and torque, which resembles a marine diesel engine used for ship propulsion. The performance validation of the proposed models' structures and the operation of Kalman Filter with experimental results, showed efficient prediction and responsiveness, since they followed the respective reference signal trajectories in a satisfactory manner. Furthermore, the concept presented in this work, could be implemented as an alternative to look-up tables (maps) from experimental data, which are already in use in the control system of the hybrid diesel-electric testbed at LME.