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통계적 방법을 이용한 DC-link 필름 커패시터의 수명 예측
신석산(Seoksan Shin),함형진(Hyeongjin Ham),이형철(Hyeongcheol Lee) 한국자동차공학회 2013 한국자동차공학회 학술대회 및 전시회 Vol.2013 No.11
In electronic vehicles (EVs) or hybrid electronic vehicles (HEVs), an inverter system has a DC-link capacitor to buffer the high frequency current. A film capacitor has been used as the DC-link capacitor in high level power system. The performance of the film capacitor has deteriorated over operating time. This can be one of critical factors in the EVs and HEVs. For this reason, the lifetime and reliability of the film capacitor are key factors to show the stability of the vehicle inverter system. Therefore, a lot of methods to predict the lifetime of the film capacitor using physical or chemical equations have been researched. However, these researches have a problem about robustness of uncertainty. In this paper, the lifetime and stability of the film capacitor are guaranteed by stochastic methods. A new model combined by random variable deterioration model and gamma process deterioration model to consider natural and sudden loss of the film capacitor is proposed. the parameters of each model are obtained by using curve fitting algorithm and method of moment. Using these models and algorithms, the lifetime of the film capacitor is predicted. Comparing experiment result and the proposed prediction model proves the proposed method has good accuracy.
인프라 연계 자율주행 실도로 실증(FOT) 데이터 분석에 대한 연구
이진서(Jinseo Lee),박부근(Bookeun Park),신석산(Seoksan Shin),강호준(Hojoon Kang) 한국자동차공학회 2023 한국자동차공학회 학술대회 및 전시회 Vol.2023 No.11
An autonomous driving technology improves rapidly, various autonomus driving services are introduced. However, there are still emerging problems regarding sudden situations such as the vehicle stagnation and accidents. In order to improve the safety of the autonomous vehicle on the road and ensure smooth autonomous driving, it is necessary to conduct the demonstration, evaluation, and analysis on the road that linking the autonomous vehicle and infrastructure. In this paper, to analyze data related to the autonomous vehicle and infrastructure, data is collected from the vehicle and infrastructure regarding unexpected situations that occur while the autonomous vehicle is driving. Through this, data analysis verifies sensors recognition rate based on the collected data. In addition, it explains how the driving performance of the autonomous vehicle can be improved by complementing problems that are difficult to solve with sensors by connecting the vehicle and infrastructure.