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이주인(Jooin Lee),이형철(Hyeongcheol Lee) 한국자동차공학회 2019 한국자동차공학회 부문종합 학술대회 Vol.2019 No.5
Recently, the technologies for reducing fuel consumption and greenhouse gas emission have attracted much attention at automobile industry. Especially in urban areas where traffic volume is concentrated, environmental problems are more serious and important. With this situation the `Intelligent Transportation System(ITS)` has been developed to collect traffic information and provide it to vehicle and drivers. The traffic information collected at ITS can applied to the vehicle controller to effectively reduce fuel consumption. This Paper proposes path finding algorithm for determining the path, which minimizes fuel consumption, using vehicle model and traffic information. This fuel-optimal route decision algorithm estimates travel-time and travel-fuel-consumption and uses `Dynamic Programming(DP)` for optimization. The cost function used in DP is designed to minimize the fuel consumption used throughout the path. The fuel-optimal route decision algorithm is verified using AIMSUN intersection model that reflects the actual traffic environment.
교차로 형상 정보를 반영한 연비 최적 속도 프로파일 결정 알고리즘
이주인(Jooin Lee),허남(Nam Heo),윤희수(Heesu Youn),이형철(Hyeongcheol Lee) 한국자동차공학회 2017 한국자동차공학회 부문종합 학술대회 Vol.2017 No.5
This paper presents the method for determining the speed profile to minimize fuel consumption using information on the intersection structure. Numerous previous papers have suggested that fuel consumption can be minimized by driving at constant speed. The driving pattern in urban area have frequent deceleration, acceleration, and idling that make unnecessary fuel consumption and carbon emissions. An individual vehicle moving along the urban street are difficult to drive at constant speed because of a lot of obstructions consisting of traffic light, queue, and intersection structure. Fuel consumption is greatly influenced by road curve, and acceleration characteristic. Therefore, optimal speed profile can be computed using intersection structure information, vehicle dynamics model. This algorithm simulated on several intersections with various shape and various traffic environments. The simulation environment is developed by microscopic traffic simulation tool and the proposed algorithm is realized by using Python. Simulations show that the information about intersection and traffic condition provides benefits in terms of fuel economy.
확장형 칼만 필터를 활용한 자율주행 전기차의 통합 고장 진단 알고리즘
이현창(Hyunchang Lee),이주인(Jooin Lee),이형철(Hyeongcheol Lee) 한국자동차공학회 2023 한국자동차공학회 학술대회 및 전시회 Vol.2023 No.11
It is important to obtain an accurate sensor value because an autonomous vehicle controls by processing various information through a sensor. If a sensor value problem occurs due to a sensor failure, it can cause a serious performance degradation problem. To prevent this deterioration problem, sensor failures are diagnosed and model-based fault detection and diagnosis algorithms are utilized as a method. In previous studies, a generalized observer method using extended kalman filter(EKF) was used as a model-based fault diagnosis method, but the computational amount increased as the number of diagnostic subjects increased. In this paper, a new method for the reduction number of extended kalman filter(EKF) was investigated. The proposed method designs the extended kalman filter(EKF) by integrating the lower system dynamics of the vehicle with vehicle dynamics.
수소연료전지 버스의 비선형 모델 예측 제어를 활용한 실시간 동력 분배 제어 알고리즘
이승연(Seungyeon Lee),이주인(Jooin Lee),임종원(Jongwon Lim),이형철(Hyeongcheol Lee) 한국자동차공학회 2023 한국자동차공학회 학술대회 및 전시회 Vol.2023 No.11
In this paper, power distribution control algorithm for fuel cell electric bus was developed. Power distribution control algorithm is developed by using Gradient based Model Predictive Control(GRAMPC). Algorithm considered vehicle’s system characteristics like fuel cell, battery, motor and vehicle dynamics. Cost function is developed with considering battery’s State-of-charge and consumption ratio of fuel cell. GRAMPC can execute nonlinear model predictive control algorithm with real-time environment. Driving scenario is developed including real world traffic condition by using SUMO and Autonomie. Algorithms is validated in real-time simulation environment with dSPACE RCP-HIL simulator.
RISF 기반의 Robust Fault Diagnosis 알고리즘
김경준(Kyungjun Kim),이주인(Jooin Lee),이형철(Hyeongcheol Lee) 한국자동차공학회 2018 한국자동차공학회 학술대회 및 전시회 Vol.2018 No.11
With the growth of Advanced Driver Assistance System(ADAS), the automobile has been changed from traditional mechanical systems to electric/electronic control systems. For accurate control, it is essential to estimate of the vehicle states correctly. If the sensed value is incorrect, it can be a great safety hazard. The most important part to consider in Fault Detection and Isolation(FDI) Algorithm design is the false alarm rate. To reduce the false alarm rate, it could be a good way to design an algorithm more robust. Especially vehicle has high non-linearity due to tire slip or lock, body slip or cornering stiffness. These factors make it hard to estimate correct longitudinal and lateral velocity. In this paper, Reliability Indexed Sensor Fusion is applied to make a more robust fault diagnosis algorithm. In addition, Generalized Observer Scheme is also applied to detect and isolate the vehicle sensor fault.
언택트 스토어 차량 구동시스템에 대한 통합 고장진단/대응 알고리즘
표현우(Hyunwoo Pyo),이주인(Jooin Lee),이형철(Hyeongcheol Lee) 한국자동차공학회 2022 한국자동차공학회 학술대회 및 전시회 Vol.2022 No.11
This paper proposes an integrated fault diagnosis and tolerance algorithm of electric autonomous vehicle. Autonomous vehicle equipped with a variety of sensors to perform vehicle safety systems and advance driver assist system (ADAS). Therefore fault diagnosis and tolerance for sensor must be developed for driver and passenger safety. The important actuator and sensors in drive system consist of PMSM, resolver, wheel speed sensor and acceleration sensor. The First step is to find analytic redundancy for the driving system with incidence matrix, Dulmage-Mendelsohn decomposition matrix and Causality Matrix. Result of the analytic redundancy derives sensor residual for sensor estimation. On next step, we build sensor fusion algorithm using kalman filter. Combine the wheel speed sensor and acceleration sensor, PMSM current sensor, motor position sensor and Wheel speed sensor. Compare estimation value and covariance to build the fault detection algorithm. On final step, change the value of the covariance of sensor fusion algorithm which isolates the fault sensor. Reconstruct the sensor signal for fault tolerance control.
Track-to-Track 센서 퓨전 방법을 이용한 고장 검출 알고리즘
김경준(Kyungjun Kim),이주인(Jooin Lee),이형철(Hyeongcheol Lee) 한국자동차공학회 2018 한국자동차공학회 부문종합 학술대회 Vol.2018 No.6
Recently, in the automotive industry, one of the most important mainstream is the intelligent vehicle. The intelligent vehicle acquires the information of the vehicle and its environments such as lane and a preceding vehicle. Then it provides convenience to the driver by using ADAS (Advanced Driver Assistance System) controller, for instance, LKA (Lane Keeping Assist), SCC (Smart Cruise Control), and FCA (Forward Collision-Avoidance Assist). It is important to get accurate sensing data of the vehicle and the environment. ISO26262 (“Road Vehicles- Functional safety”) is defined to address possible hazards of S/W and H/W for Automotive Electric/Electronic Systems. This paper deals with the FDI (Fault Detection and Isolation) algorithm to figure out the sensor faults of ADAS sensors and In-Vehicle Sensors such as IMU and Wheel Speed Sensors. Since it is hard to secure hardware redundancies of ADAS sensors, it creates underlying states of ADAS sensors and compares it with the outputs of the EKF composed of In-vehicle sensors. As the ADAS sensors, Front Camera, Left Camera, and Right Camera were selected.