Improvement of the power to weight ratio for an induction traction motor using design of experiment on neural network

Demir, Uğur

doi:10.1007/s00202-020-01204-2

Cited by 9 publications

(4 citation statements)

References 48 publications

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“…If not, this motor variation is added to the previous data set and the ANN is re-trained. Then, the ANN is run again to obtain a new motor variation, and the cycle continues to check whether it meets the minimum performance requirements by analyzing it again [13,18]. The following steps that summarize the process are shown in Figure 13a,b.…”

Section: Optimization Algorithmmentioning

confidence: 99%

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Intelligent Design Optimization for Traction and Steering Motors of an Autonomous Electric Shuttle under Driving Scenarios

Demir,

Ehsani,

Demir

et al. 2024

Electronics

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Electrified autonomous vehicles have become quite popular and have a wide range of applications. The traction and steering motors to be used on an electrified autonomous vehicle are designed considering the lateral and longitudinal forces in the environment where the vehicle operates, and they are selected with extra safety margins and “over-engineering” features. This causes wastage of rare earth elements, along with both cost and energy inefficiencies. For autonomous shuttle vehicles, traction and steering performances can be analyzed based on driving scenarios. The reference speed and steering signals for the selected driving scenarios were run on a dynamic vehicle model and the minimum performance requirements for the traction and steering motors were determined. Then, the determined design parameters by DoE (Design of Experiments) were trained in two different ANN (Artificial Neural Networks) models created for motor models. The trained ANN models were run according to the minimum performance criteria and predicted motor models with new design parameters for the traction and steering motors. The performance results of the predicted traction and steering motor models showed a significant improvement in terms of the minimum performance requirements.

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Section: Optimization Algorithmmentioning

confidence: 99%

“…13, 566 11 of 31 scenarios will be run on the vehicle dynamic model to determine the minimum performance requirements (Torque-Speed-Power) for the steering motor.…”

mentioning

confidence: 99%

Intelligent Design Optimization for Traction and Steering Motors of an Autonomous Electric Shuttle under Driving Scenarios

Demir,

Ehsani,

Demir

et al. 2024

Electronics

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“…It is widely used in the literature to reduce simulation times and costs in optimization processes. In addition, it is seen that it is used to create valuable data sets for data-based optimization and to increase efficiency [24][25][26][27][28][29][30]. In this study, DoE is used to generate datasets and calculate objective functions.…”

Section: Figure 3 Teardown Studies and Detailed Component Explanation...mentioning

confidence: 99%

Comparative Design Improvement of the Growing Rod for the Scoliosis Treatment Considering the Mechanical Complications

et al. 2023

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In this study, the focus is on an implant used in the treatment of early-onset scoliosis called magnetically controlled growing rods (MCGR). The primary goal of the study is to address and propose solutions for the mechanical problems reported in the literature concerning MCGR. The problems of the MCGR are mainly due to excessive stress and mechanical bearing problems. Therefore, an MCGR removed from a patient is teardown and geometrically modeled. Then, eleven design parameters are determined on the MCGR for the mechanical problems experienced and these are evaluated by mechanical analysis over 14 control points. In this study, analysis processes are carried out with L12 orthogonal array for eleven design parameters and 2 levels using Taguchi's experimental design method (DoE). With the obtained data by analyzing the experiments in L12, the fitness functions depending on the design parameters are created for 14 control points. Since the problem is multi-objective, a non-dominated sorting genetic algorithm (NSGA II) and multi-objective particle swarm optimization (MOPSO) are used to minimize stress and displacement in existing mechanical problems using fitness functions. The obtained design models from NSGA II and MOPSO are analyzed and evaluated in comparison with the existing mechanical model obtained through preoptimization teardown study of MCGR.

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“…One of the main problems in the driving cycle is that the uphill and downhill tests cannot be observed, so it is tried to analyze the sudden acceleration and deceleration components in the driving cycles, although not fully, the effects of these factors. In addition, there are some analysis studies in the literature [75], which are designed in ECE R15, for the downhill and climbing analysis. A driving cycle with slope scenario is given in Figure 2.…”

Section: Driving Cycles and Scenariosmentioning

confidence: 99%

Investigation on Different Driving Cycle and Scenarios Considering the Autonomous Electric Vehicles

Demir

Kocabiçak

2022

International Journal of Automotive Science and Technology

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This study presents a series of analyzes considering the traction and steering demands of an autonomous electric vehicle (AEV) as a shuttle. The considered analyzes in here are dealt with as driving cycle (DC) and driving scenarios (DS) to assess the traction and steering performance of the AEV. The aim of this study is to evaluate the issues such as over engineering for AEV traction and steering motor requirements on a certain route by comparatively analyzing traditional and dynamic calculation under the DC and DS. Therefore, DC and DS in the lit-erature are evaluated in terms of different applications, optimization techniques, generation algorithm, parametric characterization, e-motor type etc. Afterwards, NEDC, US06, WLTC, Double Lane Change (DLC), Constant Radius (CR) and Slowly Increase Steer (SIS) are determined. Then, they are arranged according to the vehicle-specific limits on an electric golf car. The modified DCs and DSs are run on the dynamic model of the vehicle. In the performed analysis, the parame-ters such as reference trajectory tracking, yaw angle, tractive and steering forces, lateral and longitudinal displacement-acceleration, steering and traction motor power–speed-torque are investigated. And the obtained results are evaluated by comparing the traditional calculation results.

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Improvement of the power to weight ratio for an induction traction motor using design of experiment on neural network

Cited by 9 publications

References 48 publications

Intelligent Design Optimization for Traction and Steering Motors of an Autonomous Electric Shuttle under Driving Scenarios

Intelligent Design Optimization for Traction and Steering Motors of an Autonomous Electric Shuttle under Driving Scenarios

Comparative Design Improvement of the Growing Rod for the Scoliosis Treatment Considering the Mechanical Complications

Investigation on Different Driving Cycle and Scenarios Considering the Autonomous Electric Vehicles

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