Optimisation of the induction motor for the tram propulsion application

Jedan od načina optimizacije dizajna nekog stroja je korištenje optimizacijskih algoritama poput mravlje kolonije. Unutar ovog diplomskog rada cilj je bio optimizirati tramvajski motor tramvaja TMK 2200. Optimizacijski algoritam mravlje kolonije pokušava pronaći minimum neke funkcije uzimajući u obzir ograničenja koja moraju biti zadovoljena u svakom prihvatljivom rješenju. Ideja algoritma proizlazi iz prirode u kojoj mravi pokušavaju pronaći najkraći put do hrane. Prilikom izvođenja optimizacija prvobitni cilj maksimizacije je gustoća snage dok su dodatne optimizacije gustoća momenta, korisnost kao i minimizacija cijene. Također, provode se i optimizacije s više ciljeva maksimizacije te se kreiraju Pareto fronte. Aksijalna duljina stroja, radijalna duljina stroja i broj zavoja samo su neki od primarnih varijabli optimizacije. Funkcije ograničenja zahtijevaju da svaki stroj ima maksimalnu snagu stroja P≥100 kW, maksimalni moment M≥705 Nm. Također, u razmatranja ulaze samo strojevi s korisnosti od η≥90 % kao i gustoćom struje J≤20 A/〖mm〗^2. Dodatni uvjet iznosio je da rubna brzina bude u rasponu od 1500≤n≤1900 o/min. U rezultatima izrađene su mape korisnosti, kao i grafovi struje, temperature te ukupnih gubitaka. Na samom kraju rada uspoređeni su svi modeli motora kao i dizajn motora u kojem je cilj bio postići najbolji motor bez korištenja optimizacijskog algoritma. Prilikom optimizacije, zbog ograničenja vezanih uz trajanje izračuna nije razmatran termički model motora. U konačnici najprihvatljiviji motor kao i onaj koji zadovoljava sve prvobitne zahtjeve je motor iz Pareto optimizacije s dva cilja maksimizacije, za koji je na kraju verificiran provedbom izračuna ciklusa vožnje.One way to optimize the design of a machine is to use optimization algorithms like an ant colony. Within this thesis, the goal was to optimize the tram motor of the TMK 2200 propulsion application. The optimization algorithm of the ant colony tries to find the function minimum taking into account the constraints that must be satisfied in every acceptable design. The motivation for the algorithm comes from nature, where ants try to find the shortest path to food. When performing optimizations, the initial goal of maximization is power density, and additional optimizations are torque density, efficiency, and cost minimization. Also, optimizations with multiple maximization goals are carried out, and Pareto fronts are plotted. Axial machine length, radial machine length, and number of turns are just some of the primary optimizing variables. The constraint functions require each machine to have a maximum shaft power P≥100 kW, a maximum shaft torque M≥705 Nm. Also, only machines with an efficiency of η≥90 % and a current density of J≤20 A/〖mm〗^2 are considered. An additional condition was that the corner speed should be in the range of 1500≤n≤1900 rpm. In the results, efficiency maps were plotted as well as graphs of the current, temperature and total losses. At the end of the thesis, all motor models were compared, together with the motor initially designed without an optimization algorithm. During the optimization calculations, the thermal model of the motor was not considered due to the excessive execution time. Ultimately, the most acceptable and selected motor that meets all the original requirements is obtained from the Pareto front with two maximization objects. At the very end, thermal duty cycle calculations were conducted for the selected motor, which served as an additional verification that the selected motor was properly designed for the tram propulsion application