@article{SchneiderVoegeleEndisch2019, author = {Schneider, Dominik and V{\"o}gele, Ulrich and Endisch, Christian}, title = {Model-based sensor data fusion of quasi-redundant voltage and current measurements in a lithium-ion battery module}, volume = {2019}, pages = {227156}, journal = {Journal of Power Sources}, number = {440}, publisher = {Elsevier}, address = {New York}, issn = {1873-2755}, doi = {https://doi.org/10.1016/j.jpowsour.2019.227156}, year = {2019}, language = {en} } @article{KleinerStuckenbergerKomsiyskaetal.2021, author = {Kleiner, Jan and Stuckenberger, Magdalena and Komsiyska, Lidiya and Endisch, Christian}, title = {Advanced monitoring and prediction of the thermal state of intelligent battery cells in electric vehicles by physics-based and data-driven modeling}, volume = {7}, pages = {31}, journal = {Batteries}, number = {2}, publisher = {MDPI}, address = {Basel}, issn = {2313-0105}, doi = {https://doi.org/10.3390/batteries7020031}, year = {2021}, abstract = {Novel intelligent battery systems are gaining importance with functional hardware on the cell level. Cell-level hardware allows for advanced battery state monitoring and thermal management, but also leads to additional thermal interactions. In this work, an electro-thermal framework for the modeling of these novel intelligent battery cells is provided. Thereby, a lumped thermal model, as well as a novel neural network, are implemented in the framework as thermal submodels. For the first time, a direct comparison of a physics-based and a data-driven thermal battery model is performed in the same framework. The models are compared in terms of temperature estimation with regard to accuracy. Both models are very well suited to represent the thermal behavior in novel intelligent battery cells. In terms of accuracy and computation time, however, the data-driven neural network approach with a Nonlinear AutoregRessive network with eXogeneous input (NARX) shows slight advantages. Finally, novel applications of temperature prediction in battery electric vehicles are presented and the applicability of the models is illustrated. Thereby, the conventional prediction of the state of power is extended by simultaneous temperature prediction. Additionally, temperature forecasting is used for pre-conditioning by advanced cooling system regulation to enable energy efficiency and fast charging.}, language = {en} } @article{TheilerEndischLewerenz2021, author = {Theiler, Michael and Endisch, Christian and Lewerenz, Meinert}, title = {Float Current Analysis for Fast Calendar Aging Assessment of 18650 Li(NiCoAl)O2/Graphite Cells}, volume = {7}, pages = {22}, journal = {Batteries}, number = {2}, publisher = {MDPI}, address = {Basel}, issn = {2313-0105}, doi = {https://doi.org/10.3390/batteries7020022}, year = {2021}, abstract = {Float currents are steady-state self-discharge currents after a transient phase—caused by anode overhang, polarization, etc.—is accomplished. The float current is measured in this study with a standard test bench for five 18650 cells (Samsung 25R) at potentiostatic conditions while the temperature is changed in 5 K steps from 5 °C to 60 °C. The entire test is performed in about 100 days resulting in 12 measurement points per cell potential for an Arrhenius representation. The float current follows the Arrhenius law with an activation energy of about 60 kJ/mol. The capacity loss measured at reference condition shows a high correlation to the results of float currents analysis. In contrast to classical calendar aging tests, the performed float current analysis enables determining the aging rate with high precision down to at least 10 °C. Returning from higher temperatures to 30 °C reference temperature shows reducing float currents at 30 °C for increasing temperature steps that may originate from an hysteresis effect that has to be investigated in future publications.}, language = {en} } @article{MayerClassenEndisch2021, author = {Mayer, Sebastian and Classen, Tobias and Endisch, Christian}, title = {Modular production control using deep reinforcement learning: proximal policy optimization}, volume = {32}, journal = {Journal of Intelligent Manufacturing}, number = {8}, publisher = {Springer Nature}, address = {Cham}, issn = {1572-8145}, doi = {https://doi.org/10.1007/s10845-021-01778-z}, pages = {2335 -- 2351}, year = {2021}, abstract = {EU regulations on CO2 limits and the trend of individualization are pushing the automotive industry towards greater flexibility and robustness in production. One approach to address these challenges is modular production, where workstations are decoupled by automated guided vehicles, requiring new control concepts. Modular production control aims at throughput-optimal coordination of products, workstations, and vehicles. For this np-hard problem, conventional control approaches lack in computing efficiency, do not find optimal solutions, or are not generalizable. In contrast, Deep Reinforcement Learning offers powerful and generalizable algorithms, able to deal with varying environments and high complexity. One of these algorithms is Proximal Policy Optimization, which is used in this article to address modular production control. Experiments in several modular production control settings demonstrate stable, reliable, optimal, and generalizable learning behavior. The agent successfully adapts its strategies with respect to the given problem configuration. We explain how to get to this learning behavior, especially focusing on the agent's action, state, and reward design.}, language = {en} } @article{MoralesTorricosBergFiggemeieretal.2025, author = {Morales Torricos, Pablo and Berg, Sascha and Figgemeier, Egbert and Endisch, Christian and Lewerenz, Meinert}, title = {The important role of lithiation-induced pressure change on apparent aging of lithium-ion batteries during accelerated cyclic aging tests}, volume = {2025}, pages = {117323}, journal = {Journal of Energy Storage}, number = {131, Part A}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2352-1538}, doi = {https://doi.org/10.1016/j.est.2025.117323}, year = {2025}, abstract = {In assessing the lifespan of lithium-ion batteries in laboratory conditions, the measured capacity declines during accelerated cyclic aging tests but increases when the cells are left idle. This recoverable capacity loss, known in literature as apparent aging, results from an inhomogeneous lithium distribution in the anode appearing in dV/dQ and capacity difference analysis. This study explores the origin of the apparent aging by correlating lithium distribution in the anode and recoverable capacity loss, with the lithiation induced pressure change and generated electrolyte motion during cycling. To this end, we compressed five commercial pouch lithium-ion cells (NMC/Gr) with holder stiffnesses ranging from 0.9 to 15.4 MPa/mm and offset pressure from 289 to 681 kPa and subjected them to continuous cyclic aging tests. While offset pressure had no significant impact, we observed, that holder stiffnesses over 3.3 MPa/mm induces an apparent capacity loss, which is recoverable by 35 \% during a resting phase. The root cause is the so-called electrolyte motion induced salt inhomogeneity (EMSI), driven by continuous high current cycling and electrolyte motion, which is intensified by higher holder stiffness but not by offset pressure. These insights are crucial for improving the accuracy of accelerated aging tests and enhancing battery lifespan predictions.}, language = {en} } @article{YuKiranEhrensbergeretal.2025, author = {Yu, Qing and Kiran, Iqra and Ehrensberger, Moritz and Streck, Luiza and Roth, Thomas and Jossen, Andreas and Endisch, Christian and Lewerenz, Meinert}, title = {Assigning Calendar Aging during Open Circuit and Float Current Analysis to Anode and Cathode Aging Using Three-Electrode Cells}, volume = {172}, pages = {060523}, journal = {Journal of the Electrochemical Society}, number = {6}, publisher = {IOP Publishing}, address = {Bristol}, issn = {1945-7111}, doi = {https://doi.org/10.1149/1945-7111/ade295}, year = {2025}, abstract = {Analyzing calendar aging by float current analysis, instead of solely repeating capacity tests, gains more insights into aging. However, the underlying mechanisms of the recharge current required to keep the voltage constant are not yet fully understood. Therefore, this study performs float current and additional open circuit voltage (OCV) tests on 3-electrode cells for four different voltages at 30 °C. By monitoring the potentials of the anode and the cathode over time individually, the aging rate on both electrodes and the present capacity can be calculated based on adjusting the electrode-specific OCV curves according to the voltage slippery theory. The electrode-specific aging and the capacity prediction show high agreement among both methods and the capacity tests at the end of the experiment. As a result, the capacity loss appears linear-like and is associated with the loss of active lithium mainly due to SEI formation on the anode. Additionally, a significant cathode voltage decay is observed especially for higher cell voltage. According to the literature, this is associated with cathode lithiation where the conductive salt is decomposed by inserting active lithium into the cathode masking SEI losses. Finally, OCV and float current analysis return comparable results and support present 2-electrode evaluation approaches.}, language = {en} } @inproceedings{RauscherStenzelEndisch2025, author = {Rauscher, Andreas and Stenzel, Peer and Endisch, Christian}, title = {Investigation of Factors Influencing the End-of-Line Partial Discharge Testing of Electrical Machines}, booktitle = {2025 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD)}, publisher = {IEEE}, address = {Piscataway}, doi = {https://doi.org/10.1109/WEMDCD61816.2025.11014117}, year = {2025}, language = {en} } @article{SchreiberLinSommeretal.2025, author = {Schreiber, Markus and Lin, Yilei and Sommer, Alessandro and Wassiliadis, Nikolaos and Morales Torricos, Pablo and Rogge, Marcel and Lewerenz, Meinert and Grosu, Cristina and Endisch, Christian and Jossen, Andreas and Lienkamp, Markus}, title = {Apparent vs. true battery aging: Impact of various load characteristics on accelerated aging tests}, volume = {2025}, pages = {116924}, journal = {Journal of Energy Storage}, number = {127}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2352-1538}, doi = {https://doi.org/10.1016/j.est.2025.116924}, year = {2025}, abstract = {Vehicle applications involve highly dynamic and variable operating conditions. These result from changing driving profiles and diverse driving behaviors. To accelerate battery aging tests, experiments are often based on constant load cycles. While efficient, this approach distorts the natural degradation behavior of the cells. This study compares cell aging under real-world and laboratory load conditions. For this purpose, we compare realistic driving profiles measured in a test vehicle in the field with synthetic constant power cycles. Average discharge power and charging sequences are kept identical for comparability. Results show that constant power cycles cause stronger cell aging than dynamic loads. Differential voltage analysis indicates more lithium inventory loss and active material loss at the negative electrode under static loads. Static loading also leads to strong inhomogeneity in the negative electrode and altered charge transfer and diffusion kinetics, as shown by impedance data. After 15 months of rest, the cells recovered up to 52 of lost capacity and 66 of the resistance increase. Impedance assessment and differential voltage analysis confirm the recovery and rehomogenization of the negative electrode. These findings show that conventional accelerated tests are strongly influenced by relaxation and load dynamics. As a result, their applicability to specific use cases, such as automotive applications, is strongly limited . Therefore, andraditional test strategies require reconsideration and redesign.}, language = {en} } @article{AzzamAydinEndischetal.2025, author = {Azzam, Mohamed and Aydin, Atakhan and Endisch, Christian and Sauer, Dirk Uwe and Lewerenz, Meinert}, title = {Float Current Analysis for Lithium-Ion Battery Aging: Insights into SEI Growth and Cathode Lithiation with EIS and ICP OES}, volume = {172}, pages = {070529}, journal = {Journal of The Electrochemical Society}, number = {7}, publisher = {IOP Publishing}, address = {Bristol}, issn = {1945-7111}, doi = {https://doi.org/10.1149/1945-7111/adef85}, year = {2025}, abstract = {This study investigates calendar-aging mechanisms in lithium-ion batteries, focusing on cathode lithiation due to decomposition of conductive salt and SEI growth, by correlating quantified float currents, capacity loss rates, and pulse resistances with changes in electrochemical impedance spectroscopy (EIS) spectra. Seven SamSung 25 R cells are aged at different float voltages with periodic EIS measurements at 30 °C. Using a pre-characterization cell, the internal processes via EIS are allocated across various states of charge and temperatures and GITT measurements are performed to derive scaling factors. GITT, float currents and capacity loss rate measurements at 30 °C enables the separation of SEI growth ISEI growth and cathode lithiation current ICL based on float current behavior across a temperature range of 5 °C to 50 °C. The distribution of relaxation times (DRT) method is employed to deconvolute overlapping electrochemical processes. EIS and DRT analyses showed significant changes in cathode charge transfer resistance and diffusion, confirming that cathode lithiation correlates substantially to elevated internal resistance at high cell voltages. The theory of ISEI growth and ICL is further supported using inductively coupled plasma atomic emission spectroscopy by quantifying elemental inventory changes and linking phosphorus release and lithium consumption to degradation mechanisms.}, language = {en} } @inproceedings{GankinMayerZinnetal.2021, author = {Gankin, Dennis and Mayer, Sebastian and Zinn, Jonas and Vogel-Heuser, Birgit and Endisch, Christian}, title = {Modular Production Control with Multi-Agent Deep Q-Learning}, booktitle = {Proceedings: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-2989-1}, doi = {https://doi.org/10.1109/ETFA45728.2021.9613177}, year = {2021}, language = {en} } @article{SchmidKneidingerEndisch2020, author = {Schmid, Michael and Kneidinger, Hans-Georg and Endisch, Christian}, title = {Data-driven fault diagnosis in battery systems through cross-cell monitoring}, volume = {21}, journal = {IEEE Sensors Journal}, number = {2}, publisher = {IEEE}, address = {New York}, issn = {1558-1748}, doi = {https://doi.org/10.1109/JSEN.2020.3017812}, pages = {1829 -- 1837}, year = {2020}, language = {en} } @article{KleinerSinghSchmidetal.2020, author = {Kleiner, Jan and Singh, Randeep and Schmid, Michael and Komsiyska, Lidiya and Elger, Gordon and Endisch, Christian}, title = {Influence of heat pipe assisted terminal cooling on the thermal behavior of a large prismatic lithium-ion cell during fast charging in electric vehicles}, volume = {2021}, pages = {116328}, journal = {Applied Thermal Engineering}, number = {188}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1359-4311}, doi = {https://doi.org/10.1016/j.applthermaleng.2020.116328}, year = {2020}, language = {en} } @article{SchmidGebauerEndisch2021, author = {Schmid, Michael and Gebauer, Emanuel and Endisch, Christian}, title = {Structural analysis in reconfigurable battery systems for active fault diagnosis}, volume = {36}, journal = {IEEE Transactions on Power Electronics}, number = {8}, publisher = {IEEE}, address = {New York}, issn = {1941-0107}, doi = {https://doi.org/10.1109/TPEL.2021.3049573}, pages = {8672 -- 8684}, year = {2021}, language = {en} } @inbook{AltmannshoferEndisch2017, author = {Altmannshofer, Simon and Endisch, Christian}, title = {Robuste Parametersch{\"a}tzung f{\"u}r reale Anwendungen}, booktitle = {Intelligente Verfahren: Identifikation und Regelung nichtlinearer Systeme}, editor = {Schr{\"o}der, Dierk and Buss, Martin}, edition = {2., erweiterte Auflage}, publisher = {Springer Vieweg}, address = {Berlin}, isbn = {978-3-662-55327-5}, doi = {https://doi.org/10.1007/978-3-662-55327-5_12}, pages = {399 -- 472}, year = {2017}, language = {de} } @article{WiedererLeitnerEndischetal.2016, author = {Wiederer, Julian and Leitner, Lukas and Endisch, Christian and Reiss, Hans}, title = {Fast Crank-Angle Based 0D Simulation of Combustion Engine Cold Tests including Manufacturing Faults and Production Spread}, volume = {9}, journal = {SAE International Journal of Passenger Cars - Mechanical Systems}, number = {1}, publisher = {SAE International}, address = {Warrendale}, issn = {1946-4002}, doi = {https://doi.org/10.4271/2016-01-1374}, pages = {321 -- 335}, year = {2016}, language = {en} } @article{JardanStumpfVargaetal.2016, author = {J{\´a}rd{\´a}n, Rafael K. and Stumpf, P{\´e}ter and Varga, Zolt{\´a}n and Endisch, Christian and Sipos, P{\´e}ter and Simon, Mikl{\´o}s}, title = {Laboratory system for measurement of iron losses in high speed drives}, volume = {41}, journal = {International Journal of Hydrogen Energy}, number = {29}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0360-3199}, doi = {https://doi.org/10.1016/j.ijhydene.2016.01.061}, pages = {12650 -- 12658}, year = {2016}, language = {en} } @article{StolzeKaramanakosKenneletal.2014, author = {Stolze, Peter and Karamanakos, Petros and Kennel, Ralph and Manias, Stefanos and Endisch, Christian}, title = {Effective variable switching point predictive current control for ac low-voltage drives}, volume = {88}, journal = {International Journal of Control}, number = {7}, publisher = {Taylor \& Francis}, address = {London}, issn = {0020-7179}, doi = {https://doi.org/10.1080/00207179.2014.942699}, pages = {1366 -- 1378}, year = {2014}, language = {en} } @inproceedings{OkonOpferkuchEndisch2020, author = {Okon, Michael and Opferkuch, Johannes and Endisch, Christian}, title = {Modular gear transmission model for analyzing production tolerances in electric drivetrains}, booktitle = {Proceedings, 2020 IEEE International Conference on Industrial Technology}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-5754-2}, doi = {https://doi.org/10.1109/ICIT45562.2020.9067240}, pages = {709 -- 716}, year = {2020}, language = {en} } @inproceedings{GerngrossKohlerEndischetal.2020, author = {Gerngroß, Martin and Kohler, Markus and Endisch, Christian and Kennel, Ralph}, title = {Model-based control of nonlinear wire tension in dynamic needle winding processes}, booktitle = {Proceedings, 2020 IEEE International Conference on Industrial Technology}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-5754-2}, doi = {https://doi.org/10.1109/ICIT45562.2020.9067168}, pages = {381 -- 388}, year = {2020}, language = {en} } @inproceedings{KleinerKomsiyskaElgeretal.2019, author = {Kleiner, Jan and Komsiyska, Lidiya and Elger, Gordon and Endisch, Christian}, title = {Modelling of 3D temperature behavior of prismatic lithium-ion cell with focus on experimental validation under battery electric vehicle conditions}, booktitle = {2019 25th International Workshop on Thermal Investigations of ICs and Systems (THERMINIC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-2078-2}, doi = {https://doi.org/10.1109/THERMINIC.2019.8923604}, year = {2019}, language = {en} }