Browse Topic: Exhaust emissions
A cold start occurs when the engine is cranked after being off for a long time, enough for its temperature to drop down to the cold ambient levels. Cold start in an engine is a critical phase as it is characterized by elevated emissions. During a cold start, exhaust components such as catalytic converter do not operate in its optimal temperature zone leading to reduced efficiency in emission control. New regulations for engine emissions are becoming stringent for this condition, hence it is important to accurately determine cold start condition in an engine to optimize the emissions control strategy. Accurate engine off time calculation plays a crucial role in cold start detection, emissions control and On-Board Diagnostics (OBD-II) decision making. This engine off time if greater than 6 hours indicates one of the conditions to confirm a cold start. Other conditions such as Ambient temperature and coolant temperature along with the engine off time confirms a cold start. This paper
In Diesel engine exhaust after treatment system (ATS), Nitrogen Oxides (NOx) emissions control is achieved via Selective Catalytic Reduction (SCR) in which AdBlue or Diesel Exhaust Fluid (DEF) plays vital role. But AdBlue freezes below -11°C due to which in cold climate conditions system performance becomes critical as it affects efficiency as well as overall performance leading to safety and compliance with emission standards issue. So, it is essential to have a probabilistic thermal model which can predict the AdBlue temperature as per ambient temperature conditions. The present paper focuses on developing Bayesian Network (BN) based algorithm for AdBlue system by modelling probability of key factors influencing on its performance including AdBlue temperature, Ambient temperature, Coolant temperature, Coolant flow, Vehicle operating conditions etc. The BN Model predicts and ensures continuous learning and improvement of the system, based on operational data. Methodology proposed in
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