Adaptive unscented Kalman filter for input estimations in Diesel-engine selective catalytic reduction systems

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• 作者：Erming CaoAuthor Vitae ; Kai Jiang ; ^{kaichiang912@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Input estimations ; Adaptive unscented Kalman filter ; Diesel-engine ; Selective catalytic reduction (SCR) system
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：12 September 2016
• 年：2016
• 卷：205
• 期：Complete
• 页码：329-335
• 全文大小：1026 K

文摘

To tackle the challenge of more and more stringent emission regulations, a selective catalytic reduction (SCR) system is widely used all over the world in Diesel-engine applications. In SCR system, input states may be indispensable for onboard diagnostic strategy. Conventionally, the NO_x and ammonia input informations are measured by several sensors, however, physical sensors are too costly for application. Besides, sensors would also increase the burden of diagnosis. Inspired by this problem, in this paper, an adaptive unscented Kalman filter (AUKF) is designed to estimate the input concentrations, due to the excellent capacity to deal with nonlinear system and calculate the noise covariance matrices online. Go a step further, the physical sensors can be replaced by the AUKF-based observer. Simulation results through the vehicle simulator cX-Emission show that the performance of observer based on AUKF is outstanding, and the estimation error is very small.