Probabilistic Thermal Modelling of AdBlue System Using Bayesian Networks Daimler Truck Innovation Center

Format:

Book

Conference/Event

Author/Creator:

Thakur, Shivam, author.

Contributor:

Salunke, Omkar

Conference Name:

SAENIS TTTMS Thermal Management Systems Conference (2025-11-06 : Guwahati, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

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 et cetera The BN Model predicts and ensures continuous learning and improvement of the system, based on operational data. Methodology proposed in the paper aims to demonstrate a probabilistic model that captures the interactions affecting the AdBlue system's thermal behavior

Notes:

Vendor supplied data

Publisher Number:

2025-28-0370

Access Restriction:

Restricted for use by site license