AbstractIn the West, the current political roadmap aims to move to a low carbon economy and, in particular, to reduce pollution associated with transportation systems. This has resulted in increased pressure on manufacturers to reduce their vehicle fleets harmful emissions. This work focuses on the use of advanced control and optimisation to reduce vehicle emissions whilst taking into account the desire for an enjoyable driving experience. The vehicle systems considered are the gear ratio and the gear shift map.
A new efficient and effective problem formulation has been developed to optimise gear ratio and gear selection, first independently and then in combination. Traditional as well as two novel objectives have been developed to capture engineering requirements such as reducing emission, maintaining or improving the vehicle driveability, promoting the durability of transmission components whilst simultaneously meeting problem specific constraints. The first novel objective formulation rewards fuel efficient engine operating points and the second objective rewards the time spent in higher gears to reduce fuel consumption. A Pareto based multi objective optimisation strategy has been adopted to identify the relative trade-off between the different objectives.
A new problem specific operator was designed, to reduce CO2 emissions by
shifting, towards the left side to promote rapid gear shifting.
Three nature inspired optimisation algorithms have been developed and critically
evaluated against the Interior-Point Optimization (Fmincon), and the
Multi-Objective Genetic Algorithm (MOGA) from the MATLAB toolbox. Multi-
Objective hybrid Cuckoo Search (MOCS) is used to optimise gear ratio. MOGA
combined with the new problem specific operator and constraint handling optimised gear shift map. Finally MOGA was combined with MOCS operator for
gear shift map optimisation. Optimised gear shift maps were implemented on a
vehicle and tested on a rolling road, following an NEDC cycle. The benefit of
the optimisation procedure being developed was demonstrated and resulted in
reduction of CO2 emissions by 2.5%.
|Date of Award||2016|
|Supervisor||Keith Burnham (Supervisor), Olivier Haas (Supervisor) & Vincent Ersanilli (Supervisor)|