Electrical Engineer for Machine Learning Battery Model
Vor 3 Tagen
Your Responsibilities:
- Develop an electrical equivalent circuit of a battery (ECC) State Space Model (SS-Model) on time invariant model parameters (R & C constant).
- Implement and compare 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.
- Compare at least two different ML algorithms (NN mandatory) to R & C parameter estimation.
- Time variant model parameters (R & C) (optional).
- Explore how prior physical knowledge can be used to improve the prediction (optional).
Your Profile:
- Ongoing studies in the fields of 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:
- You will have the opportunity to write your thesis independently and receive professional guidance and support from our experienced employees.
- You will have the chance to exchange ideas with experts in the company and benefit from their expertise.
- Take the opportunity to immerse yourself in the world of AVL and embed your theoretical knowledge in a practical environment.
The successful completion of the thesis is remunerated with a one-time fee of tax. 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.
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