30 hp - Predictive Control of Electric Machine Cooling Systems for Extended Lifespan and Performance

Introduction

Join Scania’s powertrain controls division for a thesis opportunity focused on predictive electric machine cooling within our cooling and lubrication control system team. You’ll work hands-on with heavy-duty product data and simulations, benefiting from input and mentorship from experienced engineers. If you are enthusiastic about control engineering, electric machines, and sustainable innovation we invite you to apply for this thesis position. We invite you to participate in our commitment to advancing a more sustainable and innovative future.

Background:

The thermal behaviour of electric machines significantly influences their aging process and overall reliability. Temperature fluctuations over time accelerate wear and degradation of critical components, reducing operational lifespan. Traditional cooling strategies often react to instantaneous conditions rather than anticipating future thermal loads, which can lead to suboptimal performance and premature aging.

By leveraging predictive models based on anticipated driving or load profiles and compiled historical data, it is possible to actively regulate the cooling system in a way that minimizes thermal stress. This proactive approach could positively impact the machine’s durability and maintain high performance throughout its lifecycle.

Objective:

The purpose of this thesis is to explore how predictive cooling control can be designed and implemented to improve electric machine lifespan and performance. The work will focus on:

• Considering different solutions for predictive cooling control using anticipated road slope and system historical data.
• Investigating the potential impact of predictive cooling on electric machine aging.
• A comparative study of predictive cooling effect on electric machine aging and performance.
• Evaluating how robust such control can be under varying operational conditions.
• Identifying challenging scenarios where predictive control may require adaptation.

Job description:

Through utilizing road cycle data and powertrain simulations:

• Develop a conceptual framework for predictive cooling control.
• Explore and quantify how much thermal stress on an electric machine can be reduced in heavy-duty applications through predictive cooling strategies.
• Analyze scenarios with high load variability and extreme operating conditions to determine the potential improvement in thermal management and its effect on machine longevity and performance.
• Provide insights into robustness and limitations of the predictive cooling control under different scenarios.

Education/program/focus:

Indicate education, program or focus: Currently enrolled in a master's program in mechanical engineering, electrical engineering, mechatronics engineering, or a related field.

Number of students: 1-2 students

Start date for the thesis work: flexible

Estimated time required: flexible, typically 20 weeks

Contact persons and supervisors:

Hjalmar Sventelius, Group Manager, +46855381474, 
hjalmar.Sventelius@scania.com 
Gokul Sivaraman, Development engineer, +46700877942,
gokul.sivaraman@scania.com
Christopher Ringström, Product owner and development engineer, +46855352573,
christopher.ringstrom@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.