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Thesis - General comparison of ML methods for Li-Ion battery voltage prediction
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Aktualität: 01.05.2024
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01.05.2024, AVL List GmbH
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Thesis - General comparison of ML methods for Li-Ion battery voltage prediction
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.)
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
Fachrichtung
Art des Angebots
Standorte
Thesis - General comparison of ML methods for Li-Ion battery voltage prediction
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