This chapter focuses on a design methodology that aids in design and development of complex engineering systems. This design methodology consists of simulation, optimization and decision making.Within this work a framework is presented in which modelling, multi-objective optimization and multi criteria decision making techniques are used to design an engineering system. Due to the complexity of the designed system a three-step design process is suggested. In the first step multi-objective optimization using genetic algorithm is used. In the second step a multi attribute decision making process based on linguistic variables is suggested in order to facilitate the designer to express the preferences. In the last step the fine tuning of selected few variants is performed. Thismethodology is named as Progressive Design Methodology (PDM). The method is applied as a case study to design a permanent magnet brushless DC motor drive and the results are compared with experimental values.@en

To simulate electric vehicles, appropriate battery models are required. In this work a model of the battery and a methodology for determining the parameters of a battery model using the catalog data of the batteries and Multiobjective Optimization is proposed. The technique is applied to different battery types and the results are presented.@en

This paper surveys some basics of the electric vehicle and battery charging technology. The focus is on battery charging technology and battery modelling for application in electric vehicle. Two different modelling methods are surveyed in the paper: steady state modelling and dynamic modelling of the batteries. Consequence of fast charging on the round trip efficiency is shown on steady state and dynamic states.@en

A hybrid wind park concept which contains both constant speed wind turbine and variable speed wind turbine is proposed in this paper, as it can provide similar power quality and similar low voltage ride through capability with relatively low investment. The objective is to optimize the configuration of a hybrid wind park which has low levelised production cost, high power quality, high reliability and high low voltage ride through capability. This multi-criteria multi-objective optimization problem is solved by a design approach ldquoProgressive Design Methodologyrdquo, which is divided into three stages. The final solution has the lowest levelised production cost with acceptable flicker level and LVRT capability.@en

This work focuses on a design methodology that aids in design and development of complex engineering systems. This design methodology consists of simulation, optimization and decision making. Within this work a framework is presented in which modelling, multi-objective optimization and multi criteria decision making techniques are used to design an engineering system. Due to the complexity of the designed system a three-step design process is suggested. In the first step multi-objective optimization using genetic algorithm is used. In the second step a multi attribute decision making process based on linguistic variables is suggested in order to facilitate the designer to express the preferences. In the last step the fine tuning of selected few variants are performed. This methodology is named as progressive design methodology. The method is applied as a case study to design a permanent magnet brushless DC motor drive and the results are compared with experimental values.@en

In this paper, we develop a comprehensive model of a permanent-magnet brushless DC (BLDC) motor. An analytical model for determining instantaneous air-gap field density is developed. This instantaneous field distribution can be further used to determine the cogging torque, induced back electromotive force, and iron losses in the motor. The advantage of analytical models is that they can be readily used for optimization of BLDC motor because they are fast.@en

The process of mutation has been studied extensively in the field of biology and it has been shown that it is one of the major factors that aid the process of evolution. Inspired by this a novel genetic algorithm (GA) is presented here. Various mutation operators such as small mutation, gene mutation and chromosome mutation have been applied in this genetic algorithm. In order to facilitate the implementation of the above-mentioned mutation operators a modified way of representing the variables has been presented. It resembles the way genetic information is coded in living beings. Different mutation operators pose a challenge as regards the determination of the optimal rate of mutation. This problem is overcome by using adaptive mutation operators. The main purpose behind this approach was to improve the efficiency of GAs and to find widely distributed Pareto-optimal solutions. This algorithm was tested on some benchmark test functions and compared with other GAs. It was observed that the introduction of these mutations do improve the genetic algorithms in terms of convergence and the quality of the solutions. Keywords Genetic algorithms - Multi-objective optimization - Mutations@en