Thesis Opportunity in Machine Learning for Li-Ion Battery Voltage Prediction
vor 1 Woche
YOUR RESPONSIBILITIES:
- Develop an electrical equivalent circuit of a battery (ECC) State Space Model (SS-Model) with time-invariant model parameters (R & C constant).
- Implement and compare different machine learning algorithms, including NN, LSTM, Decision Trees, Gaussian Processes, and Feature Engineering, for system identification to predict the output voltage of a Li-Ion battery.
- Train and validate the ML algorithms on a synthetic data set generated by an ECC.
- Compare at least two different ML algorithms (NN mandatory) to R & C parameter estimation.
- Explore time-variant model parameters (R & C) (optional).
- Investigate how prior physical knowledge can be used to improve the prediction (optional).
YOUR PROFILE:
- Ongoing studies in Electrical Engineering, Computer Science/Data Science, or Digital Engineering.
- Good knowledge of English.
- Programming skills in Python.
- Knowledge of optimization methods and machine learning.
WE OFFER:
- Independent thesis work with professional guidance and support from experienced employees.
- Opportunity to exchange ideas with experts in the company and benefit from their expertise.
- Chance to immerse yourself in the world of AVL and apply theoretical knowledge in a practical environment.
The successful completion of the thesis is remunerated with a one-time fee, tax-free.
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 an Equal Opportunity Employer, we consider all qualified applicants without regard to ethnicity, religion, gender, sexual orientation, or disability status.
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