• 전기학회논문지P
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• 제61권3호
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• pp.129-133
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• 2012

As people are getting concerned to Environment recently, researches on the environmentally-friendly and effective railway system have been conducted in every aspects. Especially as it became known that the pattern of train driving causes the difference in energy consumption, the researches on the train driving to minimize the energy consumption are gaining a lot of interest. The main study showed the optimal driving to minimize energy consumption while driving after analyzing real driving data measured by EMU of Bundang-line real driving, determining the impact on energy consumption due to train driving pattern changes, executing a variety of simulation on real driving patterns by Matlab Simulink and finally driving between stations by given driving times.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2005년도 춘계학술대회 논문집
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• pp.225-230
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• 2005

This paper describes determination of optimal lubricant quantity for driving gear unit. The purpose of selecting optimal lubricant quantity is to evaluate durability of driving gear unit. Lubricant quantity of driving gear unit is important factor affecting durability. The determination methode of lubricant quantity evaluation is used calculation necessary lubricant quantity first, then selection of optimal oil quantity as a base for moving of oil temperature according to changing oil quantity.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.62-65
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• 2004

An optimal robust controller design method for gun driving system is discussed in this paper. The parameters of the gun driving controller are tuned by using the LQR characteristics for the performance and robustness. Tuning method that optimize velocity error gives a significant improvement over the existing PID tuning methods. It is shown that the tuning result of real gun driving system which is regarded as rigidness model or stiffness model satisfy performance and robustness.

• Journal of applied mathematics & informatics
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• 제30권1_2호
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• pp.1-14
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• 2012

In this paper, the problem of determining the optimal car-deriving strategy has been examined. In order to find the optimal driving strategy, we have modified a method based on measure theory. Further, we demonstrate that the modified method is an efficient and practical algorithm for dealing with optimal control problems in a canonical formulation.

• 한국자동차공학회논문집
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• 제7권6호
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• pp.182-197
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• 1999

Described in this paper is an optimal driving control algorithm which focused on the improvement of fuel economy and the minimization of pollutant emissions in the parallel type hybrid drivertrain system for transit bus. For the energy balance among components such as engine, induction machine and buttery, the algorithm for power split ration determine is proposed. When it is implemented in the hybrid electric control unit(HECU) , using the sub-optimal method and the approximate technique , it is possible to save the memory , to shorten the calculation time, and to achieve the efficient driving actually. A Shift strategy for automated manual transmission is the other side of the driving control algorithm. It enables to select the optimal gear by using several shift maps which were predefined from the proposed method in this paper, As a results of driving simulation, it is proved that these algorithms make the hybrid drivetrain system to reduce fuel consumption and emissions considerably and to have the ability to the efficient use of battery.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1989년도 하계종합학술대회 논문집
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• pp.16-20
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• 1989

In this paper, we describe the optimal driving method and magnetic flux distribution of permanent magnet which enhance torque characteristics in small-sized 3-phase brushless DC motors. The disadvantages of conventional $120^{\circ}$ constant current drive method are torque ripple, switching noise and spike voltage due to the inductance of stator coil. This shortcommings can be avoided by the switching slew-rate of driving current which is called linear voltage driving method. The aim of this study is to analyze linear voltage driving method quantatively and to determine optimal drive current waveform through computer simulation. The selection of commutation angle and slew rate of a new driving current at switching instants makes torque ripple index minimize and average torque maximize. And the validity of this new driving method was assured by Fourier analysis. Considering two dimensional nonlinear magnetic flux distribution on the permanent magnet, we suggest optimal flux distribution according to the presented driving method which improves torque characteristics.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2011년도 춘계학술대회 논문집
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• pp.888-894
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• 2011

This paper is a study of optimal train driving strategy to minimize the energy consumption. Optimal driving strategy can be analyzed as an optimal problem which have constraints by using Largrangian Function and Kuhn-Tucker condition. We simulate the section between Konkuk University Station and Seongsu Station which is on outer circle line of the Seoul Metro line No.2 by using MATLAB and consider the straight level track and the speed limit.

• 한국소음진동공학회논문집
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• 제22권9호
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• pp.861-869
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• 2012

This paper presents a high performance position controller in a driving system using a time optimal control which is widely used to control driving systems to a desired reference position or velocity in minimum response time. The main purpose of this study is an improvement of transient response performance rather than steady-state response comparing with another various control strategies. In order to improve the performance of time optimal control, we tried to find the cause of the steady-state error in the driving system we have already made up and also suggest the newly modified type of time optimal control method in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권12호
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• pp.5842-5861
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• 2019

A current autonomous vehicle determines its driving strategy by considering only external factors (Pedestrians, road conditions, etc.) without considering the interior condition of the vehicle. To solve the problem, this paper proposes "An Optimal Driving Support Strategy(ODSS) based on an Genetic Algorithm for Autonomous Vehicles" which determines the optimal strategy of an autonomous vehicle by analyzing not only the external factors, but also the internal factors of the vehicle(consumable conditions, RPM levels etc.). The proposed ODSS consists of 4 modules. The first module is a Data Communication Module (DCM) which converts CAN, FlexRay, and HSCAN messages of vehicles into WAVE messages and sends the converted messages to the Cloud and receives the analyzed result from the Cloud using V2X. The second module is a Data Management Module (DMM) that classifies the converted WAVE messages and stores the classified messages in a road state table, a sensor message table, and a vehicle state table. The third module is a Data Analysis Module (DAM) which learns a genetic algorithm using sensor data from vehicles stored in the cloud and determines the optimal driving strategy of an autonomous vehicle. The fourth module is a Data Visualization Module (DVM) which displays the optimal driving strategy and the current driving conditions on a vehicle monitor. This paper compared the DCM with existing vehicle gateways and the DAM with the MLP and RF neural network models to validate the ODSS. In the experiment, the DCM improved a loss rate approximately by 5%, compared with existing vehicle gateways. In addition, because the DAM improved computation time by 40% and 20% separately, compared with the MLP and RF, it determined RPM, speed, steering angle and lane changes faster than them.

• 한국CDE학회지
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• 제21권2호
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• pp.34-38
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• 2015

In case of the big city, choosing the adequate root of which we can reach the destination can affect the driver's condition and driving time. so it is quite important to find the optimal routes for arriving the destination as considering the factors, such as driving conditions or travel time and so on. In this paper, we develop route choice model with considering driving conditions and travel time, and it can search the optimal path which make drivers reduce their fatigues using genetic algorithm.