Modelling and reduction techniques for studies of integrated hybrid vehicle systems

LS Louca, BU Yildir - Mathematical and Computer Modelling of …, 2006 - Taylor & Francis
LS Louca, BU Yildir
Mathematical and Computer Modelling of Dynamical Systems, 2006Taylor & Francis
Models of integrated vehicle systems are essential for designing hybrid vehicles by means
of simulation-based optimization. Given the complexity of hybrid vehicle systems, designing
is a time consuming process that requires the evaluation of a large number of different
design configurations. Modelling and simulation can significantly reduce the design time
through efficient design evaluations and reduced number of prototypes built. This work
presents the development and reduction of an integrated hybrid vehicle model composed of …
Models of integrated vehicle systems are essential for designing hybrid vehicles by means of simulation-based optimization. Given the complexity of hybrid vehicle systems, designing is a time consuming process that requires the evaluation of a large number of different design configurations. Modelling and simulation can significantly reduce the design time through efficient design evaluations and reduced number of prototypes built. This work presents the development and reduction of an integrated hybrid vehicle model composed of an engine, drivetrain, hydraulics and vehicle dynamics subsystems. For model development the bond graph formulation is used because it facilitates the integration of component/subsystem models in different energy domains, supports hierarchical modelling and allows straightforward manipulation of the model. The model is configured for a medium size military truck, and implemented in the 20SIM modelling and simulation environment. After developing the model, an energy-based model reduction methodology is applied in order to generate a reduced vehicle model that provides more design insight. The generated reduced system model for the hybrid truck (compared to the full model) produces almost identical predictions, has almost half the size and calculates the system response 2.5 times faster. This computationally efficient reduced model can be used for vehicle design studies to further reduce the development time.
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