@article{KleinerKomsiyskaElgeretal.2019, author = {Kleiner, Jan and Komsiyska, Lidiya and Elger, Gordon and Endisch, Christian}, title = {Thermal modelling of a prismatic lithium-ion cell in a battery electric vehicle environment}, volume = {13}, pages = {62}, journal = {Energies}, subtitle = {influences of the experimental validation setup}, number = {1}, publisher = {MDPI}, address = {Basel}, issn = {1996-1073}, doi = {https://doi.org/10.3390/en13010062}, year = {2019}, abstract = {In electric vehicles with lithium-ion battery systems, the temperature of the battery cells has a great impact on performance, safety, and lifetime. Therefore, developing thermal models of lithium-ion batteries to predict and investigate the temperature development and its impact is crucial. Commonly, models are validated with experimental data to ensure correct model behaviour. However, influences of experimental setups or comprehensive validation concepts are often not considered, especially for the use case of prismatic cells in a battery electric vehicle. In this work, a 3D electro-thermal model is developed and experimentally validated to predict the cell's temperature behaviour for a single prismatic cell under battery electric vehicle (BEV) boundary conditions. One focus is on the development of a single cell's experimental setup and the investigation of the commonly neglected influences of an experimental setup on the cell's thermal behaviour. Furthermore, a detailed validation is performed for the laboratory BEV scenario for spatially resolved temperatures and heat generation. For validation, static and dynamic loads are considered as well as the detected experimental influences. The validated model is used to predict the temperature within the cell in the BEV application for constant current and Worldwide harmonized Light vehicles Test Procedure (WLTP) load profile.}, language = {en} } @article{KomsiyskaBuchbergerDiehletal.2021, author = {Komsiyska, Lidiya and Buchberger, Tobias and Diehl, Simon and Ehrensberger, Moritz and Hanzl, Christian and Hartmann, Christoph and H{\"o}lzle, Markus and Kleiner, Jan and Lewerenz, Meinert and Liebhart, Bernhard and Schmid, Michael and Schneider, Dominik and Speer, Sascha and St{\"o}ttner, Julia and Terbrack, Christoph and Hinterberger, Michael and Endisch, Christian}, title = {Critical review of intelligent battery systems}, volume = {14}, pages = {5989}, journal = {Energies}, subtitle = {challenges, implementation, and potential for electric vehicles}, number = {18}, publisher = {MDPI}, address = {Basel}, issn = {1996-1073}, doi = {https://doi.org/10.3390/en14185989}, year = {2021}, abstract = {This review provides an overview of new strategies to address the current challenges of automotive battery systems: Intelligent Battery Systems. They have the potential to make battery systems more performant and future-proof for coming generations of electric vehicles. The essential features of Intelligent Battery Systems are the accurate and robust determination of cell individual states and the ability to control the current of each cell by reconfiguration. They enable high-level functions like fault diagnostics, multi-objective balancing strategies, multilevel inverters, and hybrid energy storage systems. State of the art and recent advances in these topics are compiled and critically discussed in this article. A comprising, critical discussion of the implementation aspects of Intelligent Battery Systems complements the review. We touch on sensing, battery topologies and management, switching elements, communication architecture, and impact on the single-cell. This review contributes to transferring the best technologies from research to product development.}, language = {en} } @article{ErikssonThomassonEkbergetal.2018, author = {Eriksson, Lars and Thomasson, Andreas and Ekberg, Kristoffer and Reig, Alberto and Eifert, Mark and Donatantonio, Fabrizio and D'Amato, Antonio and Arsie, Ivan and Pianese, Cesare and Otta, Pavel and Held, Manne and V{\"o}gele, Ulrich and Endisch, Christian}, title = {Look-ahead controls of heavy duty trucks on open roads — six benchmark solutions}, volume = {2019}, journal = {Control Engineering Practice}, number = {83}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1873-6939}, doi = {https://doi.org/10.1016/j.conengprac.2018.10.014}, pages = {45 -- 66}, year = {2018}, language = {en} } @article{LiebhartSatzkeKomsiyskaetal.2020, author = {Liebhart, Bernhard and Satzke, Martin and Komsiyska, Lidiya and Endisch, Christian}, title = {Application of nonlinear impedance spectroscopy for the diagnosis of lithium-ion battery cells under various operating conditions}, volume = {2020}, pages = {228673}, journal = {Journal of Power Sources}, number = {480}, publisher = {Elsevier}, address = {New York}, issn = {1873-2755}, doi = {https://doi.org/10.1016/j.jpowsour.2020.228673}, year = {2020}, language = {en} } @article{LiebhartKomsiyskaEndisch2019, author = {Liebhart, Bernhard and Komsiyska, Lidiya and Endisch, Christian}, title = {Passive impedance spectroscopy for monitoring lithium-ion battery cells during vehicle operation}, volume = {2020}, pages = {227297}, journal = {Journal of Power Sources}, number = {449}, publisher = {Elsevier}, address = {New York}, issn = {1873-2755}, doi = {https://doi.org/10.1016/j.jpowsour.2019.227297}, year = {2019}, language = {en} } @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} }