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Parameter identification approach using improved teaching and learning based optimization for hub motor considering temperature rise

Li Yong, Wang Yuan, Zhang Taohua, Hu Han, Wu Hao

Metrology and Measurement Systems

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2023

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Vol. 30, nr 1

99--115

EN

Temperature rise of the hub motor in distributed drive electric vehicles (DDEVs) under long-time and overload operating conditions brings parameter drift and degrades the performance of the motor. A novel online parameter identification method based on improved teaching-learning-based optimization (ITLBO) is proposed to estimate the stator resistance, 𝑑-axis inductance, 𝑞-axis inductance, and flux linkage of the hub motor with respect to temperature rise. The effect of temperature rise on the stator resistance, 𝑑-axis inductance, 𝑞-axis inductance, and magnetic flux linkage is analysed. The hub motor parameters are identified offline. The proposed ITLBO algorithm is introduced to estimate the parameters online. The Gaussian perturbation function is employed to optimize the TLBO algorithm and improve the identification speed and accuracy. The mechanisms of group learning and low-ranking elimination are established. After that, the proposed ITLBO algorithm for parameter identification is employed to identify the hub motor parameters online on the test bench. Compared with other parameter identification algorithms, both simulation and experimental results show the proposed ITLBO algorithm has rapid convergence and a higher convergence precision, by which the robustness of the algorithm is effectively verified.

2

Solving a stochastic time-cost-quality trade-off problemby meta-heuristic optimization algorithms

Mohammadi Mohammad Owais, Dede Tayfun, Grzywiński Maksym

Budownictwo o Zoptymalizowanym Potencjale Energetycznym

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2022

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Vol. 11

41--48

EN

Actual time, cost, and quality of execution options for various activities within a considered project cannot be certainly determined prior to construction, there could be three different values of time and cost for each execution option, namely, optimistic value, most likely or normal value, and pessimistic value; and the quality could be described in linguistic terms.The objective of this research is to optimize time, cost, and quality of construction projects under uncertainty utilizing the program evaluation and review technique. In this study, multi-objective functions are used to decrease total project time and total project cost whilemaximizing overall project quality. For satisfying time-cost-quality trade-off optimization, a multi-objective optimization strategy is required. The non-dominating sorting-II conceptand the crowding distance computation mechanism are combined with the teaching learning-based optimization algorithm to optimize time-cost-quality optimization problems. Non-dominating sorting-II teaching learning-based optimization algorithm is coded in MATLAB to optimize the trade-off between time, cost, and quality optimization problems. In the proposed model, the non-dominating sorting-II approach and crowding distance computationmechanism are responsible for handling objectives effectively and efficiently. Teaching learning-based optimization algorithm’s teacher and learner phases ensure that the searched solution space is explored and exploited. The proposed algorithm is applied to a 13-activity example problem, and the results show that it provides satisfactory results.

3

Multi-variable optimization models for building envelope design using EnergyPlus simulation and metaheuristic algorithms

Grygierek Krzysztof, Ferdyn-Grygierek Joanna

Architecture Civil Engineering Environment

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2019

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Vol. 12, no. 2

81--90

EN

The paper presents the method of optimal design of the building envelope. The influence of four types of windows, their size, building orientation, insulation of external walls, ceiling to unheated attic and ground floor on the life cycle costs in a singlefamily building in Polish climate conditions is analyzed. The optimization procedure is developed by means of the coupling between MATLAB and EnergyPlus. The results using three metaheuristic methods: genetic algorithms, particle swarm optimization, and algorithm based on teaching and learning are compared. The analyses have shown the possibility of reducing the life cycle costs through the optimal selection of the building structure. The high initial investment (above the required standard) pays off in the long run when using a building.

4

Design of an optimal fuzzy controller of an under-actuated manipulator based on teaching-learning-based optimization

Mahmoodabadi Mohammad Javad, Yazdizadeh-Baghini Amineh

Acta Mechanica et Automatica

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2019

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Vol. 13, no. 3

166--172

EN

In this paper, an optimal fuzzy controller based on the Teaching-Learning-Based Optimization (TLBO) algorithm has been presented for the stabilization of a two-link planar horizontal under-actuated manipulator with two revolute (2R) joints. For the considered fuzzy control method, a singleton fuzzifier, a centre average defuzzifier and a product inference engine have been used. The TLBO algorithm has been implemented for searching the optimum parameters of the fuzzy controller with consideration of time integral of the absolute error of the state variables as the objective function. The proposed control method has been utilized for the 2R under-actuated manipulator with the second passive joint wherein the model moves in the horizontal plane and friction forces have been considered. Simulation results of the offered control method have been illustrated for the stabilization of the considered robot system. Moreover, for different initial conditions, the effectiveness and the robustness of the mentioned strategy have been challenged.