CONTROL STRATEGY AND MODELING FOR ENGINE OIL SYSTEM

The power of an engine is not only used to drive the mechanical equipment connected to the engine, but also parts of the engine itself. One of these parts is the oil pump. The minimum needed oil pressure, produced by the pump, varies during the operational run of the engine. The oil pump is, therefore, often over-dimensioned to ensure that the oil supply is adequate. The over-dimensioned oil pump circulates an excessive amount of oil through the system, thus putting unnecessary strain on the engine itself. A strained engine consumes more fuel. To reduce fuel consumption, the ability to control the amount of oil circulated more accurately is therefore of interest. As oil systems are non-linear, Artificial Intelligence controllers are investigated. This work developed four versions of a model of an engine oil system and evaluated a Genetic Algorithm and two versions of Differential Evolution to adjust the weights of a Neural Network that alter the weights in a PID controller. The models were created in Simscape and Simulink, and the Neural Network was created using the Neural Network toolbox in MATLAB. The Genetic Algorithm and Differential Evolution algorithms were coded in MATLAB. The results show that the accuracy of the model is important and that the use of a machine learning algorithm for controlling a fluid system is feasible.