Biblio

Application of computational intelligence methods in data analysis and optimization problems can allow feasible and optimal solutions of complicated engineering problems. This study demonstrates an intelligent analysis scheme for determination of optimal operating condition of an internal combustion engine. For this purpose, an artificial neural network learning model is used to represent engine behavior based on engine data, and a metaheuristic optimization method is implemented to figure out optimal operating states of the engine according to the neural network learning model. This data analysis scheme is used for adjustment of optimal engine speed and fuel rate parameters to provide a maximum torque under Nitrous oxide emission constraint. Harris hawks optimization method is implemented to solve the proposed optimization problem. The solution of this optimization problem addresses eco-friendly enhancement of vehicle performance. Results indicate that this computational intelligent analysis scheme can find optimal operating regimes of an engine.