Giuseppe Giacalone
Application of Sliding Window Approach for driving pattern recognition in HEV real-time control.
Rel. Daniela Anna Misul. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Meccanica (Mechanical Engineering), 2019
Abstract
The aim of this paper is to study and implement innovative Genetic Algorithm methods and develop different applications of Sliding Window Approach for driving pattern recognition inside a real-time control model for the optimization and analysis of complex hybrid vehicles, in particular (P)HEVs. The work is divided into different sections dealing respectively with the evolutionary algorithms and the with the improvement of the real time control strategy algorithm. Taking advantage of a Rule based Control Strategy (Clustering Optimization Rule Extraction) in which the optimal discretization of a 3D input domain, constituted by the vehicle velocity, vehicle acceleration and state of charge of battery (SOC) is generated and selected using genetic algorithm technique, a modification of this latter algorithm is proposed, called Stud Genetic Algorithm.
Instead of stochastic selection, as common genetic algorithm, the fittest individual which is called Stud, is combined with all the other individuals to generate the new population
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