Development of Advance Emission Control using Machine Learning in SiL Environment

Apply now » .buttontext15ec8c373d891c1a a{ border: 1px solid transparent; } .buttontext15ec8c373d891c1a a:focus{ border: 1px dashed #041e42!important; outline: none!important; } This position is within one of TRATON’s companies. Development of Advance Emission Control using Machine Learning in SiL Environment Introduction Thesis work is an excellent way to get closer to Scania and build relationships for the future. Many of today's employees began their Scania career with their degree project. Background Scania develops advanced powertrain and exhaust aftertreatment systems to achieve high efficiency and low emissions across a wide range of operating conditions. Selective Catalytic Reduction (SCR) control requires accurate dosing decisions, robust emission prediction and effective use of signals available in the vehicle control system.   Objective The objective of this master thesis is to develop and evaluate a methodology for AI-based, closed-loop SCR control. The controller shall use machine learning to determine dosing and support emission control over a representative operating cycle.   The work will use measured test data and additional data generated in a Software-in-the-Loop (SiL) environment. Inputs may include engine operating point, load, speed, temperatures, flow conditions and other control-system variables. The AI model will be benchmarked against an existing reference SCR model in SiL using a virtual truck powertrain named VTAB (Virtual Truck and Bus).   The project will establish a traceable workflow for data preparation, model training, validation and control integration. It will assess whether a data-driven model can reduce calibration effort and enable adaptable control while maintaining robust dosing and emission performance in a real powertrain.   Job description The assignment includes the following activities:  Literature study on SCR control, machine learning and AI-based control methods  Collect, structure and quality-assure large volumes of test and SiL data  Select relevant control-system signals and define model inputs, outputs and constraints  Develop and train an AI model for SCR dosing and emission control  Integrate and evaluate the model in a virtual truck powertrain SiL environment  Compare performance with an existing reference SCR control model  Analyse robustness, accuracy, limitations, calibration effort and implementation potential  Document the methodology and present the results in a master thesis report Education/program/focus Machine learning, artificial intelligence, control engineering or applied mathematics. Understanding of mathematical models and physical functions used in control systems Experience in handling, processing and analysing large data sets. We value curiosity, structured problem solving, initiative and the ability to work both independently and collaboratively.   Contact persons and supervisors  Kim Petersson, +46 (0)8 553 825 24 kim.petersson@scania.com     Application: Your application must include a CV, personal letter and transcript of grades A background check might be conducted for this position. We are conducting interviews continuously and may close the recruitment earlier than the date specified.   Requisition ID:  33683 Number of Openings:  1.0 Part-time / Full-time:  Full-time Permanent / Temporary:  Temporary Country / Region:  SE Location(s):  Södertälje, SE, 151 38 #job-location.job-location-inline { display: inline; } Required Travel:  0% Workplace:  On-site Apply now » Find similar jobs: 斯堪尼亚, Jobs bei Scania, Jobs at Scania., Jobs at Scania, Empleos en Scania Opens in a new tab. Opens in a new tab. Opens in a new tab. Opens in a new tab. Available Positions Career locations Contact us About Scania Legal notice Privacy statement Cookies Whistleblowing © Copyright Scania 2024 All rights reserved. Scania CV AB (publ), SE-151 87 Södertälje, Sweden // // // // const jobAlertSpans = document.querySelectorAll("[data-testid=jobAlertSpanText]"); jobAlertSpans?.forEach((jobEl) => { jobEl.textContent = window?.jsStr?.tcjobresultscreatejobalertsdetailstext || ""; }); // // // ')); $('.frequency-error').removeClass('hidden'); } else { alert(result.join('\n')); } } }); }); //]]> //