Predictive model for continuous optimization of DPF service interval based on economic trade-off

Abstract: AbstractEfficiency, emissions and uptime are three crucial factors that development engineers in the automotive business strive to improve. The proposed study is a part of this continuous improvement towards perfection of the Scania engine and exhaust after-treatment system. The engine-out soot gets deposited in the diesel particulate filter to avoid the release of particulate matter in the environment. This soot deposition in the filter creates an increased back pressure for the engine and thus affects the fuel consumption, engine performance as well as the life of the engine. It is thus important that soot regenerations are done periodically to clean the filter. When the soot gets oxidized, it leaves behind a small fraction of incombustible material, which is the ash. This ash gets accumulated over the time as it cannot be oxidized, thus it creates higher back pressure and increased regeneration frequency. Both these factors contribute to an increased engine fuel consumption. The goal of this master’s thesis project is to develop a predictive model that could be integrated to vehicle’s embedded system to continuously calculate and optimize the service interval for the diesel particulate filter. Optimization based on economic trade-off between the diesel particulate filter service costs against the fuel penalty costs. The model is validated with the available field test vehicle data. A new method is also developed to estimate the ash and soot fractions inside the diesel particulate filter at a given point of time, which would be used to develop a modified regeneration strategy with varying start regen threshold while being safe to avoid soot exotherm reactions in the filter.