Machine Learning to Predict Online Battery Lifetime in Electric Vehicles (3079)
Machine learning to Predict Online Battery Lifetime in Electric Vehicles (3079)
Target group: Automotive Engineering
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 Controls & Calibration 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
The battery management system (BMS) of a battery-electric road vehicle must ensure an optimal operation of the electrochemical storage system to guarantee for durability and reliability. In particular, the BMS must provide precise information about the battery’s state-of-functionality, i.e. how much dis-/charging power can the battery accept at current state and condition while at the same time preventing it from operating outside its safe operating area, namely current overload, under- and over temperature, maximum/minimum current, state-of-charge as well as under- and overvoltage. These critical limits have to be calculated in a predictive manner, which serve as a significant input factor for the supervising vehicle energy management (VEM). The VEM must provide enough power to the vehicles’ drivetrain for certain tasks and especially in critical driving situations.
Scope
- The vehicle must to know how much energy and power is available and how much the pack can absorb. These values will be calculated within the BMS based on safety and life constraints and will vary quickly (as the pack is used) and slowly (as the pack ages). Hence, the main task of the thesis work is to develop ML algorithms which improve remaining useful life estimation, the new functions depends on the quality of battery state and parameter estimation framework.
- In this work, a new approach shall be proposed based on Machine Learning, a data-driven self-learning and predictive model shall be implemented which is updating its parameters to predict battery lifetime
The students will work in the Traction battery department together with our teams of Controls & Calibration Engineers and SCRUM SW team. Weperform system level SW development and simulations.
Profile
- Strong educational background in systems and control theory including nonlinear filtering, estimation, machine learning, Matlab/Simulink
- Deep knowledge in algorithms and design
- Self-motivated and meticulous in your problem solving approach
- Communication skills are important since information will be needed from several different parts of the department
Duration
- Period: 1 semester, 30 ECTS points
- Starting date: January 2018
- Number of students: Suitable for 1-2 student
Application
- Attach your resume 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 (Manager, Traction Battery, Controls & Calibration),
Apply at
We would like to receiveyour application as soon as possible, but no later than 2017-11-24.
About Volvo Car Group
The future belongs to those who are empowered by a great idea and have the ability to carry it out. At Volvo Car Group, our vision is clear: "To be the world's most progressive and desired premiumcar brand" by simplifying people's lives. We have bold targets when it comes to innovation, sales and customer satisfaction and to make this happen, we need talented people onboard. People with passion, energy, business sense and the drive to innovate. People that want to create the next generation Volvo cars in a global, dynamic and respectful environment. We will support you to reach your full potential. Join us on this exciting journey into the future.