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Joachim Schaeffer M.Sc.

Working area(s)

Interpretable machine learning, lithium-ion batteries, features, Gaussian processes, field data, Bayesian optimization, hybrid modeling

Contact

Work S3|10 517
Landgraf-Georg-Str. 4
64283 Darmstadt

My research is at the intersection of interpretable machine learning and lithium-ion batteries.

I’m developing robust and interpretable methods to predict cycle life from early test or formation data. Furthermore, I work on the application of Gaussian processes for battery fault detection and prediction from field data.

I care about open source, open data and open science.

Google Scholar

GitHub

LinkedIn

Topic Type Status
Bayesian Optimization of Battery Fast Charging Protocols Master's Thesis completed 04/2024
Development of a BMS System for a VTOL Glider Master's Thesis completed 02/2024
Development of a State of Charge Estimation Algorithm for Lithium-Iron-Phosphate Batteries Master's Thesis completed 07/2023
Development of a Firmware for an 18650 Battery Cycler Project seminar completed 07/2023
Battery Field Data: Machine Learning and Modeling for Fault Detection Master's Thesis completed 12/2022
Felddatenanalyse und Zustands- und Parameteridentifikation einer HV-Fahrzeugbatterie Master's Thesis completed 06/2022
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