摘要：AbstractCar builders are facing restrictions on both fuel consumption (FC) and pollutant emissions. In this context, Diesel Hybrid Electric Vehicles (HEV) are an interesting solution since they are more fuel-efficient than their gasoline-powered counterparts. However, Diesel pollutants remain a concern as, despite the fact that particulate emissions are treated by the Diesel Particulate Filter (DPF), tailpipe NOx emissions are still problematic. Previous works have shown that a better FC to pollutants trade-off can be achieved if the Energy and Emissions Management Strategy (EEMS) of the HEV includes the pollutants downstream an After-Treatment System (ATS). For Diesel HEV, a previous approach which took into account the Selective Catalytic Reduction (SCR) has been applied. This paper aims to find a better trade-off by adding the Diesel Oxidation Catalyst’s (DOC) influence on the SCR’s efficiency. Consequently, three EEMS taking into account more or fewer ATS are calculated with the Dynamic Programming (DP) algorithm on the Worldwide Light vehicle Test Cycle (WLTC). Hence, each strategy is applied to the full energy and emission model in order to deduce a fuel consumption to tailpipe NOx tradeoff. The tradeoffs for each strategy are compared. As a result, all the strategies can reduce NOx emissions by 30% with a FC penalty of 3%. In addition, the three EEMS have shown similar FC/tailpipe NOx trade-offs but the EEMS that took ATS into account were able to produce 3% more engine-out NOx than the strategy with no ATS. To ensure the optimality of the solutions, the impact of the states’ discretization in the DP algorithm is investigated. Despite a high discretization level for each state, the SCR’s temperature variation is still over-approximated and might underestimate the benefit of a strategy considering the complete Diesel ATS.
关键词：KeywordsHybrid electric vehicleOptimal supervisory controlModeling of automotive systemsAfter-treatment systems
