We offer the following research topic

Thesis - General comparison of ML methods for Li-Ion battery voltage prediction 

Bachelor Thesis

In the automotive industry, SOC (State of Charge) and SOH (State of Health) estimates play an essential role for electric vehicles. These estimates increase safety and improve charging efficiency. We use machine learning  algorithms to improve these estimations and thus promote progress in battery technology for a more efficient and sustainable automotive future.

WHAT WE OFFER YOU:

• Modeling of an electrical equivalent circuit of a battery (ECC)
  • State Space Model (SS-Model) on time invariant model parameters (R & C constant)
• Implementation and comparison of different ML algorithms 
  • like NN, LSTM, Decision Trees, Gaussian Processes, Feature Engineering
  • for system identification to predict the output voltage of a Li-Ion battery
  • the ML algorithms are to be trained and validated on a synthetic data set generated by an ECC
• Comparison of at least two different ML algorithms (NN mandatory) to 
  • R & C parameter estimation
• Time variant model parameters (R & C) (optional) 
• How can prior physical knowledge be used to improve the prediction (opt.)

WHAT WE LOOK FOR:

• Good knowledge of English
• Programming skills in Python
• Knowledge of optimization methods and machine learning

WHICH STUDY TRACKS WE PREFER:

• Electrical Engineering
• Computer Science/Data Science
• Digital Engineering

The successful completion of the thesis is remunerated with a one-time fee of EUR €1,700.00 before tax.

You don't want to write your final thesis just for the books, then explore the mobility of the future together with us! Maybe you will be a part of it soon!

At AVL, we foster and celebrate diversity: We recognize that diverse ways of thinking are required to achieve our vision of a greener, safer, and better world of mobility. Different backgrounds, attitudes, interests, and experiences make us successful. As Equal Opportunity Employer we consider all qualified applicants without regard to ethnicity, religion, gender, sexual orientation or disability status.