• Journal of applied mathematics & informatics
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• v.30 no.1_2
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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.

• Proceedings of the KSR Conference
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• 2011.05a
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.5759-5779
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• 2017

As the main means to cope with the stubborn problem of drunk driving, the inspection of drunk driving has already been paid more attention and thus reinforced. In this paper, we model this scenario as a Stackelberg game, where the police department (called defender) allocates resources dynamically in terms of the traffic situation on the traffic network to arrest drink drivers and drivers who drink (called attacker), whether choosing drunk driving or designated driving service, expect to minimize their cost for given travel routes. However, with the number of resources are limited, our goal is to calculate the optimal resource allocation strategy for the defender. Therefore, first, we provide an effective approach (named OISDD) to fulfill our goal, i.e., generate the optimal strategy to inspect drunk driving. Second, we apply OISDD to directed graphs (which are abstracted from Dalian traffic network) to analyze and test its correctness and rationality. The experimental results show that OISDD is feasible and efficient.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.6
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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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.3
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• pp.73-82
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• 2017

As of December 2016, the number of registered automobiles in Korea exceeds 21million. As a result, greenhouse gas emission by transportation sector are increasing every year. It was concluded that the development of the driving strategy considering the driving behavior and the road conditions, which are known to affect the fuel efficiency and the greenhouse gas emissions, could be the most effective fuel economy improvement. Therefore, this study aims to develop a fuel efficient driving strategy in a complex linear section with uphill and curved sections. The road topography was designed according to 'Rules about the Road Structure & Facilities Standards'. Various scenarios were selected. After generating the speed profile, it was applied to the Comprehensive Modal Emission Model and fuel consumption was calculated. The scenarios with the lowest fuel consumption were selected. After that, the fuel consumption of the manual driver's driving record and the selected optimal driving strategy were compared and analyzed for verification. As a result of the analysis, the developed optimal driving strategy reduces fuel consumption by 21.2% on average compared to driving by manual drivers.

• International Journal of Railway
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• v.2 no.2
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• pp.70-79
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• 2009

An optimal driving strategy of a train in a long journey on a nonsteep track has four phases: an initial power phase, a long hold speed phase, a coast phase and a final brake phase. The majority of the journey is speed holding. On a track with steep gradients, it becomes necessary to vary the strategy around steep sections of track because it is not possible to hold a constant steep on steep track. Instead we must interrupt the speed hold phase with a power phase. The aim of this paper is to show that there is a unique power phase that satisfies the necessary conditions for an optimal journey. The problem is developed and solved for various cases, from a simple single steep gradient to a complicated multiple steep gradient section. For each case, we construct a set of new conditions for optimality of the power phase that minimises the energy used during the power phase subject to a weighted time penalty. We then use the new necessary conditions to develop a calculate scheme for finding an optimal power phase for a steep incline. We also present an example to confirm the uniqueness of an optimal power phase.

• International Journal of Automotive Technology
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• v.8 no.6
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• pp.791-798
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• 2007

The hydraulic hybrid vehicle(HHV) is an application of hydrostatic transmission technology to improve vehicle fuel economy and emissions. A relatively lower energy density of hydraulic accumulator and complicated coordinating operations between two power sources require a special energy management strategy to maximize the fuel saving potential. This paper presents a new type of configuration for parallel HHV to minimize the disadvantages of the hydraulic accumulator, as well as a methodology for developing an energy management strategy tailored specially for PHHV. Based on an analysis of the optimal energy distribution between two power sources over a representative urban driving cycle with a Dynamic Programming(DP) algorithm, a fuzzy-based optimal torque management strategy is designed and developed to control the torque distribution. Simulation results demonstrates that the optimal torque management strategy maximizes the advantages of this hybrid type of configuration, and the high power density characteristics of hydraulic technology effectively improve the robustness of the energy management strategy and fuel economy of the PHHV.

• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.3
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• pp.85-97
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• 2000

In this paper, static and dynamic shift control stategies of CVT speed ratio are suggested. For the static shift control, in order to operate engine on the optimal operating region, a fuzzy control logic is used. In the fuzzy logic, S- factor that is defined as a degree of sportiness is introduced. Simulation results show that the static shift control strategy based on the fuzzy logic selects the optimal operating point automatically between the economy and the sporty mode corresponding to the driver's desire and the driving condition. For the dynamic shift control strategy, a shift speed map is suggested which determines the shift sped as fast or slow based on Δi, the difference between the desired speed ratio id and the actual speed ratio i, and throttle opening. It is seen from the simulation results that the CVT shift speed is determined by the dynamic shift control strategy to provide appropriate performance and comfort for the driver's demand and driving condition. Additionally, experiments are performed to investigate the dynamic performance of the shift speed for the lift foot up. From the experimental results, it is found that improved shift feeling can be obtained by the dynamic shift control strategy when lift foot up occurs.

• Proceedings of the KIEE Conference
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• 1998.07a
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• pp.365-367
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• 1998

This paper shows the form of the optimal solution and how to minimize energy of train driving control. In this paper we consider the case where a train is to be driven by manual operation mode along a straight level track, and with speed limits. Using the constrained optimal technique(Lagrange Function & Kuhn-Tucker equations), we constructed optimal train driving strategy.