Development of Model Based Closed Loop Control Strategy of SCR System for Heavy-Duty Diesel Engines 2017-01-2383
Urea selective catalytic reduction (SCR) is a key technology for heavy-duty diesel engines to meet the increasingly stringent nitric oxides (NOx) emission limits of regulations. The urea water solution injection control is critical for urea SCR systems to achieve high NOx conversion efficiency while keeping the ammonia (NH3) slip at a required level. In general, an open loop control strategy is sufficient for SCR systems to satisfy Euro IV and Euro V NOx emission limits. However, for Euro VI emission regulation, advanced control strategy is essential for SCR systems due to its more tightened NOx emission limit and more severe test procedure compared to Euro IV and Euro V.
This work proposed an approach to achieve model based closed loop control for SCR systems to meet the Euro VI NOx emission limits. A chemical kinetic model of the SCR catalyst was established and validated to estimate the ammonia storage in the SCR catalyst. Based on the model, a PI controller was designed to control the ammonia storage in the SCR catalyst at the level close to the saturated condition to maximize the NOx conversion efficiency without exceeding the NH3 slip limit.
Both simulation results and engine bench test results demonstrated good effects of the control strategy. With the effects of the control strategy, the ammonia storage in the SCR catalyst can be well controlled, the NOx emission in the world harmonized transient cycle (WHTC) after the SCR system of a heavy-duty diesel engine with raw NOx emission at 8.13 g/kW·h can be reduced to about 0.3 g/kW·h, and the averaged NOx conversion efficiency was higher than 96%, while the averaged NH3 slip in the cycle kept below 10 ppm, which has met the NOx emission requirements of the Euro VI emission regulation.
Citation: Wang, G., Zhang, J., Yang, B., Li, C. et al., "Development of Model Based Closed Loop Control Strategy of SCR System for Heavy-Duty Diesel Engines," SAE Technical Paper 2017-01-2383, 2017, https://doi.org/10.4271/2017-01-2383. Download Citation
Author(s):
Guoyang Wang, Jun Zhang, Bo Yang, Chuandong Li, Shi-Jin Shuai, Shi Yin, Meng Jian
