Brushless DC Wheel Motor Research Articles

Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems

This research introduces the Multi-Objective Liver Cancer Algorithm (MOLCA), a novel approach inspired by the growth and proliferation patterns of liver tumors. MOLCA emulates the evolutionary tendencies of liver tumors, leveraging their expansion dynamics as a model for solving multi-objective optimization problems in engineering design. The algorithm uniquely combines genetic operators with the Random Opposition-Based Learning (ROBL) strategy, optimizing both local and global search capabilities. Further enhancement is achieved through the integration of elitist non-dominated sorting (NDS), information feedback mechanism (IFM) and Crowding Distance (CD) selection method, which collectively aim to efficiently identify the Pareto optimal front. The performance of MOLCA is rigorously assessed using a comprehensive set of standard multi-objective test benchmarks, including ZDT, DTLZ and various Constraint (CONSTR, TNK, SRN, BNH, OSY and KITA) and real-world engineering design problems like Brushless DC wheel motor, Safety isolating transformer, Helical spring, Two-bar truss and Welded beam. Its efficacy is benchmarked against prominent algorithms such as the non-dominated sorting grey wolf optimizer (NSGWO), multiobjective multi-verse optimization (MOMVO), non-dominated sorting genetic algorithm (NSGA-II), decomposition-based multiobjective evolutionary algorithm (MOEA/D) and multiobjective marine predator algorithm (MOMPA). Quantitative analysis is conducted using GD, IGD, SP, SD, HV and RT metrics to represent convergence and distribution, while qualitative aspects are presented through graphical representations of the Pareto fronts. The MOLCA source code is available at: https://github.com/kanak02/MOLCA.

Research on Constant Current Control of Regenerative Braking in Hybrid Energy Storage Electric Bicycle

Electric vehicle has gradually become a hot research topic due to the energy crisis and environmental pollution problems. But the traffic is becoming much more crowed because of the sharp increase in the number of private cars, for this reason, the electric bicycle has been more and more popular owing to the convenience of it, especially the electric bicycle is easier than vehicle to park, but the mileage short problem of electric bicycle is always the main factor to influence the development of electric bicycle, moreover, electric bicycles are chiefly used under urban conditions which have frequent start and stop process, so single energy source electric bicycle can not be very good to adapt to these driving conditions. So far, there is little research about the application of the super capacitor and regenerative braking system of the electric bicycle. In this paper, regenerative braking system of hybrid energy storage electric bicycle is the research object, the mathematical model of regenerative braking system of hybrid energy storage electric bicycle has been established, regenerative braking system of hybrid energy storage consists of the brushless dc motor, Buck - Boost DC-DC converter, super capacitor and controller. In order to validate the regenerative braking system model, the regenerative braking simulation test system has been designed and developed, the equivalent small power brushless dc wheel motor drive system is used to simulate the drive system of electric bicycle. The mathematical model of regenerative braking system is calculated and experimental verified, the results show that the established mathematical model is accurate and effective. In order to maintain small braking torque fluctuation range during the braking process, constant current control strategy has been employed to control the armature current. As simulation result and experiment data shows, the hybrid energy storage system can effectively absorb the regenerative braking energy. Stable braking torque can be implemented and higher energy recovery efficiency has been realized in the process by adopting the constant current control strategy.

A Robust Design Optimization Method for Electromagnetic Devices With Interval Uncertainties

High reliability is usually demanded for the design of electromagnetic devices. Interval uncertainties widely exist in the robust optimization problems, and the bounds of the design parameters can be only obtained instead of their specific distributions. To solve this kind of problem efficiently, this paper presents a robust optimization approach based on the Chebyshev interval method to estimate the extreme values of the constraints and objectives. To demonstrate the effectiveness of the method, the Testing Electromagnetic Analysis Methods workshop problem 22 and a brushless dc wheel motor optimization benchmark problem are investigated with interval uncertainties of the relevant parameters. Deterministic and robust optimizations considering the worst case with uniform sampling are also conducted for comparison. The optimization results illustrate the effectiveness of the proposed method.

Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems

This paper proposes a multi-objective version of the recently proposed Ant Lion Optimizer (ALO) called Multi-Objective Ant Lion Optimizer (MOALO). A repository is first employed to store non-dominated Pareto optimal solutions obtained so far. Solutions are then chosen from this repository using a roulette wheel mechanism based on the coverage of solutions as antlions to guide ants towards promising regions of multi-objective search spaces. To prove the effectiveness of the algorithm proposed, a set of standard unconstrained and constrained test functions is employed. Also, the algorithm is applied to a variety of multi-objective engineering design problems: cantilever beam design, brushless dc wheel motor design, disk brake design, 4-bar truss design, safety isolating transformer design, speed reduced design, and welded beam deign. The results are verified by comparing MOALO against NSGA-II and MOPSO. The results of the proposed algorithm on the test functions show that this algorithm benefits from high convergence and coverage. The results of the algorithm on the engineering design problems demonstrate its applicability is solving challenging real-world problems as well.

Electric Vehicle Regenerative Braking System Based on Constant Current Control of Composite Power Sources

Due to the characteristics of high power density existing in super capacitor,the defects of low power density in power battery can be remedied,by using super capacitor as the auxiliary power source of electric vehicle. electric vehicle regenerative braking system is acted as the research object,the mathematical models of electric vehicle regenerative braking system of composite power sources are established,consisting of the brushless dc motor,Buck-Boost DC-DC converter,super capacitor as well as controller. In order to validate the electric vehicle regenerative braking system model,the regenerative braking simulation test system is designed and developed,the small power brushless dc wheel motor drive system is used to simulate electric vehicle drive system,the flywheel moment of inertia is used to simulate electric vehicle inertia load. The mathematical models of regenerative braking system are calculated and experimental verified,the results show that the mathematical model established is accurate and effective. In order to maintain small braking torque fluctuation range during the braking process,constant current control strategy is employed to control the armature current. As simulation results and experiment data shows,electric vehicle composite power source,consisting of power battery and super capacitor,can effectively absorb the regenerative braking energy. By adopting the constant current control strategies,stable braking torque can be implemented in the process of braking,and higher energy recovery efficiency is realized.

Bat-Inspired Optimization Approach for the Brushless DC Wheel Motor Problem

This paper presents a metaheuristic algorithm inspired in evolutionary computation and swarm intelligence concepts and fundamentals of echolocation of micro bats. The aim is to optimize the mono and multiobjective optimization problems related to the brushless DC wheel motor problems, which has 5 design parameters and 6 constraints for the mono-objective problem and 2 objectives, 5 design parameters, and 5 constraints for multiobjective version. Furthermore, results are compared with other optimization approaches proposed in the recent literature, showing the feasibility of this newly introduced technique to high nonlinear problems in electromagnetics.

Multiobjective Biogeography-Based Optimization Based on Predator-Prey Approach

Biogeography is the science that studies the geographical distribution and the migration of species in an ecosystem. Biogeography-based optimization (BBO) is a recently developed global optimization algorithm as a generalization of biogeography to evolutionary algorithm and has shown its ability to solve complex optimization problems. BBO employs a migration operator to share information between the problem solutions. The problem solutions are identified as habitat, and the sharing of features is called migration. In this paper, a multiobjective BBO, combined with a predator-prey (PPBBO) approach, is proposed and validated in the constrained design of a brushless dc wheel motor. The results demonstrated that the proposed PPBBO approach converged to promising solutions in terms of quality and dominance when compared with the classical BBO in a multiobjective version.

Some results on the design of brushless DC wheel motor using SQP and GA

Some results on the design of brushless DC wheel motor using SQP and GA

Analytical model for the optimal design of a brushless DC wheel motor

PurposeAnalytical models are often used in the first steps of the design process. They are associated with optimisation methods to find a solution that fulfil the design specifications. In this paper, the analytical model of an electric motor is built and proposed as a benchmark to highlight the optimisation methods the most fitted to analytical models.Design/methodology/approachThis paper studies the optimal design of a brushless DC wheel motor. First, the analytical model is presented. Each equation used for the sizing is described, including the physical phenomenon associated, the hypotheses done, and some precautions to take before computing. All equations are ordered to ease their resolution, due to a specific procedure which is then described. Secondly, three optimisation problems with an increasing number of parameters and constraints are proposed. Finally, the results found by the sequential quadratic method point out the special features of this benchmark.FindingsThe constraint optimisation problem proposed is clearly multimodal as shown in the results of one deterministic method. Many starting points were used to initialise the optimisation methods and lead to two very different solutions.Originality/valueFirst, an analytical model for the optimal design is detailed and each equation is explained. A specific procedure is presented to order all equations in order to ease their resolution. Secondly, a multimodal benchmark is proposed to promote the development of hybrid methods and special heuristics.

Design and Control of Axial-Flux Brushless DC Wheel Motors for Electric Vehicles—Part I: Multiobjective Optimal Design and Analysis

We have applied multiobjective optimal design to a brushless dc wheel motor. The resulting axial-flux permanent-magnet motor has high torque-to-weight ratio and motor efficiency and is suitable for direct-driven wheel applications. Because the disk-type wheel motor is built into the hub of the wheel, no transmission gears or mechanical differentials are necessary and overall efficiency is thereby increased and weight is reduced. The dedicated motor was modeled in magnetic circuits and designed to meet the specifications of an optimization scheme, subject to constraints such as limited space, current density, flux saturation, and driving voltage. In this paper, two different motor configurations of three and four phases are illustrated. Finite-element analyses are then carried out to obtain the electromagnetic, thermal, and modal characteristics of the motor for modification and verification of the preliminary design. The back-electromotive forces of prototypes are examined for control strategies of current driving waveforms.

Determination Of The Phase Current Waveform For A Disc‐Type Axial‐Flux Wheel Motor

ABSTRACTAn optimal design of the driving current pattern for a disc‐type axial‐flux brushless DC wheel motor of an electric vehicle is proposed in this paper. The electro‐magnetic dynamic model of the motor is established with magnetic circuits, describing the relationship between the output torque and excitation current. The optimal current pattern, in terms of magnitude and phase angle, is then obtained by maximizing the output torque with respect to the rotor shift. Compared with the traditional three‐phase‐on current pattern of fixed 120–degree phase shift, both the average torque and efficiency with the driving current of an optimal advanced switching angle are seen to be improved under various loading conditions. The motor performance with the optimal driving waveform is simulated and verified by experiments.