Search Results

Engine-Off Natural Vacuum (EONV) principles based leak detection monitors are designed to determine the presence of a small leak in the fuel tank system. It was introduced to address the ever more stringent emission requirement (currently at 0.02”) for gasoline engine equipped vehicles as proposed by the Environmental Protection Agency (EPA) and California Air Resources Board (CARB) in the United States [2, 3]. Other environmental protection agencies including the ones in EU and China will be adopting similar regulations in the near future. Due to its sensitivity to known noise factors such as the ambient temperature, barometric pressure, drive pattern and parking angle, it has been historically a lower performing monitor that is susceptible to warranty cost or even voluntary recalls. The proposed new model based monitor utilizes production pressure signal and newly instrumented temperature sensors [15].

A three-way catalytic converter (TWC) is an emissions control device, used to treat the exhaust gases in a gasoline engine. The conversion efficiency of the catalyst, however, drops with age or customer usage and needs to be monitored on-line to meet the on board diagnostics (OBD II) regulations. In this work, a non-intrusive catalyst monitor is developed to diagnose the track the remaining useful life of the catalyst based on measured in-vehicle signals. Using air mass and the air-fuel ratio (A/F) at the front (upstream) and rear (downstream) of the catalyst, the catalyst oxygen storage capacity is estimated. The catalyst capacity and operating exhaust temperature are used as an input features for developing a Support Vector Machine (SVM) algorithm based classifier to identify a threshold catalyst. In addition, the distance of the data points in hyperspace from the calibrated threshold plane is used to compute the remaining useful life left.

Traditionally, a three-way catalyst (TWC) is controlled to a set heated exhaust gas oxygen (HEGO) sensor voltage (typically placed after the monitored catalyst) that corresponds to optimal catalyst efficiency. This limits the control action, as we rely on emissions breakthrough at the HEGO sensor to infer the state of catalyst. In order to robustly meet the super ultra-low emission regulations, a more precise TWC control around the oxidation level of catalyst is desirable. In this work, we developed a comprehensive set of models to predict the oxygen storage capacity using measured in-vehicle signals only. This is accomplished by developing three models; the first model is a linear in parameter regression model to predict the feed gas emissions from measured signals like engine speed and air-to-fuel ratio (A/F). The second model is a low-dimensional physics based model of the three-way catalyst to predict the exhaust emissions and oxidation state of the catalyst.