Nonlinear Model Predictive Control for Cold Start Selective Catalytic Reduction

摘要: Abstract Selective catalytic reduction (SCR) is emerging as a key technology for reducing emissions of nitrogen oxides (NOx) from diesel vehicles, but the temperature dependence governing chemical kinetics are highly nonlinear and industry standard techniques limiting ammonia injection until catalyst reaches operating leave room improvement NOx reduction. Cold start constitute significant fraction urban emissions, due to low road speeds short journeys precluding reaching quickly. We demonstrate that model predictive control (NMPC) provides desired performance in adhering required constraints meeting complex objectives regardless temperature. The results include improved overall during typical test cycle including cold start, without design effort specifically operation. also show controller amenable real-time implementation use vehicle.