Urea Injection Control Based on Deep-Q Networks for SCR Aftertreatment Systems
2020 · IFAC-PapersOnLine
저자
Shin Young Bae, Dong Hwi Jeong, Yeon-Soo Kim, Byung Jun Lee, Sanha Lim, Changho Jung, Chang Hwan Kim, Yong-Wha Kim, 이종민 (Jong Min Lee)
연구주제
Engine and Fuel Emissions · Reinforcement Learning in Robotics
NTIS 과학기술표준분류
가장 가까운 분류(환경 > 대기질 관리 > 미세먼지오염 개선기술)가 화학·화공 범위 밖이다. 배정 근거: AI 키워드가 잡은 NTIS 과제의 분류.
AI 연구 키워드·방법
디젤 배기 후처리 선택적 촉매 환원(SCR) urea injection control
키워드 (OpenAlex)
selective catalytic reduction deep Q-network reinforcement learning NOx emissions model predictive control real-world driving data
초록
The regulations on NOx emissions from diesel vehicles have been stringent in recent years. Various techniques such as lean NOx trap (LNT) and selective catalytic reduction (SCR) have been developed to lessen the NOx emissions. The urea-based SCR method, which utilizes NH3 as reducing agent to remove NOx, is widely used. Determining optimal amount of injected urea that keeps NOx at outlet below regulated NOx emission and also minimizes the amount of dosed urea is important. Model predictive control (MPC) is popularly used to determine the optimal amount of injected urea. However, applying MPC to real vehicle driving may be difficult because the on-line computation of MPC is too costly to be conducted in the engine control unit (ECU), the computation performance of which is significantly low at present. Therefore, reinforcement learning (RL) is considered as an alternative to on-line contr…
이 논문을 지원한 국가R&D 과제 (NTIS 성과)
NTIS 성과DB(2020–24)에서 DOI 매칭 없음
인접 그래프
노드 hover = 이웃 강조 · 1회 클릭 = 고정 · 고정된 노드 다시 클릭 = 페이지 이동