TY - JOUR A1 - Schneider, Dominik A1 - Vögele, Ulrich A1 - Endisch, Christian T1 - Model-based sensor data fusion of quasi-redundant voltage and current measurements in a lithium-ion battery module JF - Journal of Power Sources UR - https://doi.org/10.1016/j.jpowsour.2019.227156 KW - Battery model KW - Current sensor KW - Dual kalman filter KW - Sensor data fusion KW - State estimation KW - Weighted least squares Y1 - 2019 UR - https://doi.org/10.1016/j.jpowsour.2019.227156 SN - 1873-2755 VL - 2019 IS - 440 PB - Elsevier CY - New York ER - TY - JOUR A1 - Kleiner, Jan A1 - Stuckenberger, Magdalena A1 - Komsiyska, Lidiya A1 - Endisch, Christian T1 - Advanced monitoring and prediction of the thermal state of intelligent battery cells in electric vehicles by physics-based and data-driven modeling JF - Batteries N2 - 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. UR - https://doi.org/10.3390/batteries7020031 KW - lithium-ion battery KW - electro-thermal model KW - smart cell KW - intelligent battery KW - neural network KW - temperature prediction Y1 - 2021 UR - https://doi.org/10.3390/batteries7020031 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13068 SN - 2313-0105 VL - 7 IS - 2 PB - MDPI CY - Basel ER - TY - JOUR A1 - Theiler, Michael A1 - Endisch, Christian A1 - Lewerenz, Meinert T1 - Float Current Analysis for Fast Calendar Aging Assessment of 18650 Li(NiCoAl)O2/Graphite Cells JF - Batteries N2 - 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. UR - https://doi.org/10.3390/batteries7020022 KW - 18650 KW - NCA KW - graphite KW - float current KW - self-discharge KW - calendar aging KW - Arrhenius Y1 - 2021 UR - https://doi.org/10.3390/batteries7020022 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13081 SN - 2313-0105 VL - 7 IS - 2 PB - MDPI CY - Basel ER - TY - JOUR A1 - Mayer, Sebastian A1 - Classen, Tobias A1 - Endisch, Christian T1 - Modular production control using deep reinforcement learning: proximal policy optimization JF - Journal of Intelligent Manufacturing N2 - 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. UR - https://doi.org/10.1007/s10845-021-01778-z KW - modular production KW - production control KW - production scheduling KW - deep reinforcement learning KW - proximal policy optimization KW - automotive industry Y1 - 2021 UR - https://doi.org/10.1007/s10845-021-01778-z UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13092 SN - 1572-8145 VL - 32 IS - 8 SP - 2335 EP - 2351 PB - Springer Nature CY - Cham ER - TY - JOUR A1 - Morales Torricos, Pablo A1 - Berg, Sascha A1 - Figgemeier, Egbert A1 - Endisch, Christian A1 - Lewerenz, Meinert T1 - The important role of lithiation-induced pressure change on apparent aging of lithium-ion batteries during accelerated cyclic aging tests JF - Journal of Energy Storage N2 - 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. UR - https://doi.org/10.1016/j.est.2025.117323 Y1 - 2025 UR - https://doi.org/10.1016/j.est.2025.117323 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-60173 SN - 2352-1538 VL - 2025 IS - 131, Part A PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Yu, Qing A1 - Kiran, Iqra A1 - Ehrensberger, Moritz A1 - Streck, Luiza A1 - Roth, Thomas A1 - Jossen, Andreas A1 - Endisch, Christian A1 - Lewerenz, Meinert T1 - Assigning Calendar Aging during Open Circuit and Float Current Analysis to Anode and Cathode Aging Using Three-Electrode Cells JF - Journal of the Electrochemical Society N2 - 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. UR - https://doi.org/10.1149/1945-7111/ade295 Y1 - 2025 UR - https://doi.org/10.1149/1945-7111/ade295 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-60504 SN - 1945-7111 VL - 172 IS - 6 PB - IOP Publishing CY - Bristol ER - TY - CHAP A1 - Rauscher, Andreas A1 - Stenzel, Peer A1 - Endisch, Christian T1 - Investigation of Factors Influencing the End-of-Line Partial Discharge Testing of Electrical Machines T2 - 2025 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD) UR - https://doi.org/10.1109/WEMDCD61816.2025.11014117 Y1 - 2025 UR - https://doi.org/10.1109/WEMDCD61816.2025.11014117 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Schreiber, Markus A1 - Lin, Yilei A1 - Sommer, Alessandro A1 - Wassiliadis, Nikolaos A1 - Morales Torricos, Pablo A1 - Rogge, Marcel A1 - Lewerenz, Meinert A1 - Grosu, Cristina A1 - Endisch, Christian A1 - Jossen, Andreas A1 - Lienkamp, Markus T1 - Apparent vs. true battery aging: Impact of various load characteristics on accelerated aging tests JF - Journal of Energy Storage N2 - 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. UR - https://doi.org/10.1016/j.est.2025.116924 Y1 - 2025 UR - https://doi.org/10.1016/j.est.2025.116924 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59653 SN - 2352-1538 VL - 2025 IS - 127 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Azzam, Mohamed A1 - Aydin, Atakhan A1 - Endisch, Christian A1 - Sauer, Dirk Uwe A1 - Lewerenz, Meinert T1 - Float Current Analysis for Lithium-Ion Battery Aging: Insights into SEI Growth and Cathode Lithiation with EIS and ICP OES JF - Journal of The Electrochemical Society N2 - 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. UR - https://doi.org/10.1149/1945-7111/adef85 Y1 - 2025 UR - https://doi.org/10.1149/1945-7111/adef85 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-60922 SN - 1945-7111 VL - 172 IS - 7 PB - IOP Publishing CY - Bristol ER - TY - CHAP A1 - Gankin, Dennis A1 - Mayer, Sebastian A1 - Zinn, Jonas A1 - Vogel-Heuser, Birgit A1 - Endisch, Christian T1 - Modular Production Control with Multi-Agent Deep Q-Learning T2 - Proceedings: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA45728.2021.9613177 Y1 - 2021 UR - https://doi.org/10.1109/ETFA45728.2021.9613177 SN - 978-1-7281-2989-1 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Schmid, Michael A1 - Kneidinger, Hans-Georg A1 - Endisch, Christian T1 - Data-driven fault diagnosis in battery systems through cross-cell monitoring JF - IEEE Sensors Journal UR - https://doi.org/10.1109/JSEN.2020.3017812 KW - Battery systems KW - data-driven fault diagnosis KW - fault isolation KW - recursive principal component analysis KW - signal processing Y1 - 2020 UR - https://doi.org/10.1109/JSEN.2020.3017812 SN - 1558-1748 SN - 1530-437X SN - 2379-9153 VL - 21 IS - 2 SP - 1829 EP - 1837 PB - IEEE CY - New York ER - TY - JOUR A1 - Kleiner, Jan A1 - Singh, Randeep A1 - Schmid, Michael A1 - Komsiyska, Lidiya A1 - Elger, Gordon A1 - Endisch, Christian T1 - Influence of heat pipe assisted terminal cooling on the thermal behavior of a large prismatic lithium-ion cell during fast charging in electric vehicles JF - Applied Thermal Engineering UR - https://doi.org/10.1016/j.applthermaleng.2020.116328 KW - Lithium-ion battery KW - Battery modeling KW - Electro-thermal model KW - Fast charging KW - Terminal cooling KW - Heat pipe Y1 - 2020 UR - https://doi.org/10.1016/j.applthermaleng.2020.116328 SN - 1359-4311 VL - 2021 IS - 188 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Schmid, Michael A1 - Gebauer, Emanuel A1 - Endisch, Christian T1 - Structural analysis in reconfigurable battery systems for active fault diagnosis JF - IEEE Transactions on Power Electronics UR - https://doi.org/10.1109/TPEL.2021.3049573 KW - Active fault diagnosis KW - electrothermal battery model KW - minimal sensor set KW - reconfigurable battery KW - structural analysis Y1 - 2021 UR - https://doi.org/10.1109/TPEL.2021.3049573 SN - 1941-0107 VL - 36 IS - 8 SP - 8672 EP - 8684 PB - IEEE CY - New York ER - TY - CHAP A1 - Altmannshofer, Simon A1 - Endisch, Christian ED - Schröder, Dierk ED - Buss, Martin T1 - Robuste Parameterschätzung für reale Anwendungen T2 - Intelligente Verfahren: Identifikation und Regelung nichtlinearer Systeme UR - https://doi.org/10.1007/978-3-662-55327-5_12 Y1 - 2017 UR - https://doi.org/10.1007/978-3-662-55327-5_12 SN - 978-3-662-55327-5 SN - 978-3-662-55326-8 SP - 399 EP - 472 PB - Springer Vieweg CY - Berlin ET - 2., erweiterte Auflage ER - TY - JOUR A1 - Wiederer, Julian A1 - Leitner, Lukas A1 - Endisch, Christian A1 - Reiss, Hans T1 - Fast Crank-Angle Based 0D Simulation of Combustion Engine Cold Tests including Manufacturing Faults and Production Spread JF - SAE International Journal of Passenger Cars - Mechanical Systems UR - https://doi.org/10.4271/2016-01-1374 Y1 - 2016 UR - https://doi.org/10.4271/2016-01-1374 SN - 1946-4002 SN - 1946-3995 VL - 9 IS - 1 SP - 321 EP - 335 PB - SAE International CY - Warrendale ER - TY - JOUR A1 - Járdán, Rafael K. A1 - Stumpf, Péter A1 - Varga, Zoltán A1 - Endisch, Christian A1 - Sipos, Péter A1 - Simon, Miklós T1 - Laboratory system for measurement of iron losses in high speed drives JF - International Journal of Hydrogen Energy UR - https://doi.org/10.1016/j.ijhydene.2016.01.061 KW - Electrical machines and drives KW - Permanent magnet machine KW - Loss measurement KW - High speed drives Y1 - 2016 UR - https://doi.org/10.1016/j.ijhydene.2016.01.061 SN - 0360-3199 N1 - Special Issue on 3rd European Conference on Renewable Energy Systems (ECRES′2015), 7–10 October 2015, Kemer, Antalya, Turkey VL - 41 IS - 29 SP - 12650 EP - 12658 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Stolze, Peter A1 - Karamanakos, Petros A1 - Kennel, Ralph A1 - Manias, Stefanos A1 - Endisch, Christian T1 - Effective variable switching point predictive current control for ac low-voltage drives JF - International Journal of Control UR - https://doi.org/10.1080/00207179.2014.942699 KW - model predictive control (MPC) KW - optimal control KW - variable switching point KW - three-level inverter KW - ac low-voltage drives Y1 - 2014 UR - https://doi.org/10.1080/00207179.2014.942699 SN - 0020-7179 SN - 1366-5820 VL - 88 IS - 7 SP - 1366 EP - 1378 PB - Taylor & Francis CY - London ER - TY - CHAP A1 - Okon, Michael A1 - Opferkuch, Johannes A1 - Endisch, Christian T1 - Modular gear transmission model for analyzing production tolerances in electric drivetrains T2 - Proceedings, 2020 IEEE International Conference on Industrial Technology UR - https://doi.org/10.1109/ICIT45562.2020.9067240 KW - electric drivetrain modeling KW - multidomain simulation KW - modular gear transmission model KW - gear mesh dynamics Y1 - 2020 UR - https://doi.org/10.1109/ICIT45562.2020.9067240 SN - 978-1-7281-5754-2 SP - 709 EP - 716 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Gerngroß, Martin A1 - Kohler, Markus A1 - Endisch, Christian A1 - Kennel, Ralph T1 - Model-based control of nonlinear wire tension in dynamic needle winding processes T2 - Proceedings, 2020 IEEE International Conference on Industrial Technology UR - https://doi.org/10.1109/ICIT45562.2020.9067168 Y1 - 2020 UR - https://doi.org/10.1109/ICIT45562.2020.9067168 SN - 978-1-7281-5754-2 SP - 381 EP - 388 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Kleiner, Jan A1 - Komsiyska, Lidiya A1 - Elger, Gordon A1 - Endisch, Christian T1 - Modelling of 3D temperature behavior of prismatic lithium-ion cell with focus on experimental validation under battery electric vehicle conditions T2 - 2019 25th International Workshop on Thermal Investigations of ICs and Systems (THERMINIC) UR - https://doi.org/10.1109/THERMINIC.2019.8923604 Y1 - 2019 UR - https://doi.org/10.1109/THERMINIC.2019.8923604 SN - 978-1-7281-2078-2 PB - IEEE CY - Piscataway ER -