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Project Description: 

Modern vehicles increasingly rely on on-board decision-making to improve efficiency, range, and sustainability. Energy management tasks, such as charge planning for electrified powertrains and eco-speed planning along routes, are naturally formulated as constrained nonlinear optimization problems. While general purpose solvers perform well on desktops or in clouds, deploying them on embedded or edge platforms in vehicles introduces strict constraints on computation time, memory footprint, determinism, and reliability. A domain-aware solver tailored for automotive conditions can bridge the gap between advanced optimization methods and real-time, resource-constrained execution common in Swedish and European mobility contexts.

Scope

The thesis focuses on designing, implementing, and evaluating a lightweight numerical solver for constrained nonlinear optimization that is suitable for deployment on representative embedded platforms in vehicles. The solver will target typical energy management use cases and prioritize real-time feasibility, robustness, and portability.

This thesis is related to the Swedish Electromobility Center project “CHARGE” that will contribute with optimization algorithms and guidance on optimization. The two optimization problems that have been solved in CHARGE are:

1. the optimization of the charging process of an electric vehicle (i.e. the charging optimization problem)
2. the optimal energy and thermal management of an electric vehicle driving to a charging station (i.e. the driving optimization problem)

These two problems have been solved through a custom-made solver based on a combination of Pontryagin’s Maximum Principle (PMP) and Newton’s Method.

Research tasks:

1. Integration and demonstration of the two existing algorithms in a realistic vehicle environment
2. Update the PMP-based algorithms to be generic, so that they can be re-used for other optimization problems. The idea is to try to have the computational advantages of the PMP-based method while also mitigating its reduced flexibility. This shall be realized as an energy efficient speed optimizer as the alternative to the other two optimization problems.

Expected outcome:

1. Models validated in vehicle environment
2. Generic solver validated for energy efficiency speed problem
3. A working prototype solver library suitable for edge or embedded deployment in automotive environments.
4. Performance benchmarks comparing solution quality and runtime

Key focus areas: Charge planning, Energy efficient speed planning, deployment in embedded systems and solver methodology and solutions.

Your skills and background

• Ongoing MSc in Systems in Controls and Mechatronics or Mobility engineering, Data science and AI or Computer science or similar.
• Knowledge in optimization & control theory.
• Understanding of numerical optimization and control for nonlinear, constrained problems.
• Experience with programming in C/C++ for embedded targets; Python/Matlab for prototyping and analysis.
• Familiarity with embedded development practices, including cross-compilation, profiling, and basic RTOS or POSIX environments.
• Basic knowledge of vehicle energy management concepts for electrified and conventional powertrains.

Why you should join Zeekr Tech Eu

We are engineers, developers, and innovators from around the world. Joined together by entrepreneurship, our unique blend of global culture, and a belief in a smarter more sustainable future. At Zeekr Tech Eu we fast-track innovation and transform ideas into pioneering technology solutions, doing your master thesis here is no different. We are convinced that a thesis project is a major contribution to our innovation capabilities and long-term development. You'll have a great opportunity to use your skills and creativity to push the boundaries of what´s possible.

What happens when you apply
If this sounds interesting and you match the requirements, please don't hesitate to submit your application with a CV and cover letter. Shortlisted candidates will be contacted for an interview to further discuss the project's details and expectations. 

This thesis project is intended for 1-2 students

Supervisor: Niklas Legnedahl, Architect - Energy Management, niklas.legnedahl@zeekrtech.eu, please contact for more information about the project

Starting date: January 2026

Last application date: 2025-11-03

Apply today. We will perform ongoing selection during the application period. We look forward to hearing from you!

Please note that due to GDPR regulations, we can only accept applications sent through the recruitment system, not via email or other channels.