Abstract:

This thesis deals with problem of energy management in hybrid electric vehicles. Vehicle customers expect lower fuel consumption, and governments impose tight restrictions on emissions. The control objective of energy management is to lower the fuel consumption and exhaust emissions while satisfying several constraints. Particle Swarm Optimization (PSO) and its variants have been a topic of research over the past decade. Inspired by social behavior of bird flocking and fish schooling, PSO has proven to be an effective stochastic search technique. Hence it has been applied to a wide variety of problems related to search optimization, clustering, routing, scheduling. PSO has gone through various changes and different variants have been introduced in order to solve the problems more effectively. It has also been combined with other different algorithms to create hybrid optimization algorithms. These applications have been implemented using the standard PSO and two kinds of hybrid algorithms- Differential Evolution Particle Swarm Optimization (PSO-DE) and PSO with Quantum Infusion (PSO-QI). These algorithms have been reported in the literature and applied to different practical applications. Bacteria Foraging Optimization (BFO) also became a interesting topic of research and application. Inspired by foraging characteristics of E.Coli bacteria, BFO has proven to be an effective search technique. It found applications in a variety of problems related to optimization, clustering, routing, scheduling etc. Recent developments in BFO literature find various changes with different variations and hybridised forms. These applications have been implemented using the standard BFO and GBF, a kind of hybrid algorithm combining merits BFO and that of Genetic Algorithm (GA). These algorithms also reported in the literature and applied to different practical applications.