Machine Learning Engineer for Li-Ion Battery Voltage Prediction
Vor 5 Tagen
YOUR RESPONSIBILITIES:
- Develop and implement an electrical equivalent circuit of a battery (ECC) State Space Model (SS-Model) with time-invariant model parameters (R & C constant)
- Compare and evaluate different machine learning algorithms, including NN, LSTM, Decision Trees, and Gaussian Processes, for system identification and voltage prediction of Li-Ion batteries
- Estimate and compare R & C parameter values using at least two different machine learning algorithms (NN mandatory)
- Explore the impact of time-variant model parameters (R & C) on battery voltage prediction (optional)
- Investigate the potential of incorporating prior physical knowledge to enhance prediction accuracy (optional)
YOUR PROFILE:
- Ongoing studies in Electrical Engineering, Computer Science/Data Science, or Digital Engineering
- Excellent English language skills
- Proficiency in Python programming
- Knowledge of optimization methods and machine learning techniques
WE OFFER:
- Independent thesis work with professional guidance and support from experienced AVL employees
- Opportunities to collaborate with experts and benefit from their expertise
- Chance to apply theoretical knowledge in a practical environment and contribute to the development of innovative mobility solutions
The successful completion of the thesis is rewarded with a one-time fee. At AVL, we value diversity and recognize that diverse perspectives are essential for achieving our vision of a greener, safer, and better world of mobility.
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