Exploring Advanced Machine Learning Techniques for Li-Ion Battery Voltage Prediction
vor 1 Monat
We are seeking a highly motivated individual to contribute to our research project on machine learning algorithms for Li-Ion battery voltage prediction. This thesis opportunity is designed to explore the potential of various ML methods in improving SOC and SOH estimates for electric vehicles.
Key Responsibilities:- Develop and implement electrical equivalent circuit models (ECC) of a Li-Ion battery
- Train and compare different ML algorithms, including NN, LSTM, Decision Trees, and Gaussian Processes, for system identification and output voltage prediction
- Analyze and visualize the results to identify the most effective approach
Requirements:
- Ongoing studies in Electrical Engineering, Computer Science/Data Science, or Digital Engineering
- Proficiency in Python programming
- Knowledge of optimization methods and machine learning principles
About AVL:
AVL is a leading mobility technology company providing solutions for development, simulation, and testing in the automotive industry. As a thesis partner, you will have access to our expert knowledge and resources, enabling you to apply your theoretical skills in a practical environment.
Benefits:
- Independent thesis work with professional guidance and support
- Opportunity to exchange ideas with experts and benefit from their expertise
- A stipend of €1,700 before tax upon successful completion of the thesis
Your Profile:
We welcome applications from students with a strong academic background in relevant fields. If you're interested in contributing to cutting-edge research and gaining hands-on experience in the automotive industry, please apply now.
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