Share this Job
Apply now »

Thesis work: Machine Learning DRO

Let's create the future together.

At Volvo Cars, we believe that being curious and truly committed to understanding people is the key to future success. We are people who care about other people, working together to create new technologies and innovations for safe, sustainable and convenient mobility. Want to join us?

 


At RESEARCH AND DEVELOPMENT you will be a key contributor to the next generation outstanding luxury cars from Volvo. Together with other engineers around the world, you and your team will create innovative human-centric car technology that makes life less complicated and more enjoyable for people. Are you interested in design and connected car technology? Do you share our passion for people, the environment and our urge to create a superior driving experience? Research and Development is the place for you to prosper.

 

This thesis work will be carried out within the Department of Propulsion Energy Systems, which is a part of VCC´s Research and Development organization.

We are dedicated to electrify our brand in a broad effort with Plug-In Hybrids (PHEVs) and Battery Electric Vehicles (BEVs). At the engineering department of Propulsion Energy Systems, we are responsible for design and delivery of the technical solution of Traction Battery systems for electrified vehicles. We cover the complete product development of mechanical, electrical and software. We are the core competence center in Northern Europe in regards to Lithium Ion technology and automotive battery systems.

Volvo Cars Traction Battery SW group is a new group focused on the SW functionality and the Li-Ion expertize for the Traction Battery, used in electrified vehicles. We are taking major steps in taking SW-development in-house and performing more In-house calibration to reach improved results. Much of the in-house SW is based on novel, advanced control systems that needs experienced engineers to calibrate.

 

Background

During the lifetime of a battery, its performance, power and energy decreases gradually due to irreversible chemical changes which makes it very difficult to have a robust and reliable on-line estimation of the current condition. To deliver reliable performance prediction while the battery ages requires complex online calculation and high degree of models including unknown parameters.

The mobility of the future includes the ability of the car to optimize its own operation and maintenance using on-board sensors and a connection to the backend via cloud services. This will allow to transmit battery measurement data from all vehicles to form an internet-based computing platform. Analyzing vehicle data sets will help to draw conclusion regarding current performance of the battery and elements for predictive models to estimate future aging trends of the entire fleet and give the opportunity to answer basic business questions and quality assurance.   

 

Scope

The goal of this thesis is to predict the probability of occurrence of an event in the future (battery performance degradation) when we have incomplete information about the life cycle of the battery parameters from the entire fleet of cars in operation in the real world conditions.  The estimate in level of degradation of the battery through time based is based on the data available till today. There are different data sources available, from vehicle read outs performed during vehicle service and remote monitoring data which is stored in the data warehouse. Machine learning techniques shall be applied by analysing and processing real-world driving data to predict the remaining useful life of the battery.

 

 

Profile

Educational background in systems and control theory including nonlinear filtering, estimation, machine learning

Python programming language (or similar) and corresponding tool chains for data analytics

Deep knowledge in (ML) algorithms and design

Self-motivated and meticulous in your problem solving approach.

Communication skills are important since information input will be needed from several different parts of the department.

 

 

The students will work in the Traction battery department together with our teams of Controls & Calibration Engineers and SCRUM SW team. We perform system level SW development and simulations.

 

Duration

Period: 1 semester, 30 ECTS points

Starting date: January 2020

Number of students: Suitable for 1-2 student.

 

Application
Attach your resume, grades, coursework and cover letter stating your interests within the given area and your thoughts and credentials.

Please note that applications arriving later then the last application date will not be taken in consideration.

Selection will be ongoing during the application period.

 

 

In case of questions, please contact:

Christian Fleischer (Product Owner, Traction Battery) Christian.Fleischer@volvocars.com


We want your application as soon as possible.

 

Use the electronic application-form at https://group.volvocars.com/careers/students


 

 

Who are we?

Everything we do starts with people. Our purpose is to provide freedom to move, in a personal, sustainable and safe way. We are committed to simplifying our customers’ lives by offering better technology solutions that improve their impact on the world and bringing the most advanced mobility innovations to protect them, their loved ones and the people around them. 


Volvo Cars’ continued success is the result of a collaborative, diverse, and inclusive working environment. The people of Volvo Cars are committed to making a difference in our world. Today, we are one of the most well-known and respected car brands, with over 40,000 employees across the globe. We believe in bringing out the best in each other and harnessing the true power of people. At Volvo Cars your career is designed around your talents and aspirations so you can reach your full potential. Join us on a journey of a lifetime as we create safety, autonomous driving and electrification technologies of tomorrow.

Gothenburg, O, SE

Job requisition ID:  27659

Apply now »