TY - JOUR A1 - Kleiner, Jan A1 - Komsiyska, Lidiya A1 - Elger, Gordon A1 - Endisch, Christian T1 - Thermal modelling of a prismatic lithium-ion cell in a battery electric vehicle environment BT - influences of the experimental validation setup JF - Energies N2 - 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. UR - https://doi.org/10.3390/en13010062 KW - Lithium-Ionen-Batterie KW - Elektrofahrzeug Y1 - 2019 UR - https://doi.org/10.3390/en13010062 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-7532 SN - 1996-1073 VL - 13 IS - 1 PB - MDPI CY - Basel ER - TY - JOUR A1 - Komsiyska, Lidiya A1 - Buchberger, Tobias A1 - Diehl, Simon A1 - Ehrensberger, Moritz A1 - Hanzl, Christian A1 - Hartmann, Christoph A1 - Hölzle, Markus A1 - Kleiner, Jan A1 - Lewerenz, Meinert A1 - Liebhart, Bernhard A1 - Schmid, Michael A1 - Schneider, Dominik A1 - Speer, Sascha A1 - Stöttner, Julia A1 - Terbrack, Christoph A1 - Hinterberger, Michael A1 - Endisch, Christian T1 - Critical review of intelligent battery systems BT - challenges, implementation, and potential for electric vehicles JF - Energies N2 - 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. UR - https://doi.org/10.3390/en14185989 KW - smart battery KW - intelligent battery system KW - advanced monitoring KW - reconfigurable battery KW - battery management system KW - electric vehicle Y1 - 2021 UR - https://doi.org/10.3390/en14185989 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-10132 SN - 1996-1073 VL - 14 IS - 18 PB - MDPI CY - Basel 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 - CHAP A1 - Kleiner, Jan A1 - Heider, Alexander A1 - Hanzl, Christian A1 - Komsiyska, Lidiya A1 - Elger, Gordon A1 - Endisch, Christian T1 - Thermal behavior of an intelligent li-ion cell under vehicle conditions T2 - Proceedings: IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society UR - https://doi.org/10.1109/IECON43393.2020.9254336 KW - Li-ion KW - electro-thermal model KW - intelligent battery KW - reconfigurable system KW - smart cell Y1 - 2020 UR - https://doi.org/10.1109/IECON43393.2020.9254336 SN - 978-1-7281-5414-5 SN - 2577-1647 SP - 2081 EP - 2086 PB - IEEE CY - Piscataway 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 - 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 - TY - CHAP A1 - Kleiner, Jan A1 - Heider, Alexander A1 - Komsiyska, Lidiya A1 - Elger, Gordon A1 - Endisch, Christian T1 - Experimental Study on the Thermal Interactions in Novel Intelligent Lithium-Ion Modules for Electric Vehicles T2 - Proceedings of the Twentieth InterSociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems: ITherm 2021 UR - https://doi.org/10.1109/ITherm51669.2021.9503299 KW - Intelligent battery KW - Lithium-ion KW - Reconfiguration KW - Cooling KW - Thermal management KW - Parallel connection Y1 - 2021 UR - https://doi.org/10.1109/ITherm51669.2021.9503299 SN - 978-1-7281-8539-2 SN - 2694-2135 SP - 556 EP - 562 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Kleiner, Jan A1 - Lambauer, Florian A1 - Singh, Randeep A1 - Komsiyska, Lidiya A1 - Hinterberger, Michael A1 - Endisch, Christian T1 - Experimental study of cell integrated heat pipe cooling with a lithium-ion cell emulator JF - Journal of Energy Storage UR - https://doi.org/10.1016/j.est.2022.105808 Y1 - 2022 UR - https://doi.org/10.1016/j.est.2022.105808 SN - 2352-1538 SN - 2352-152X VL - 2022 IS - 56, Part A PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Schmid, Michael A1 - Liebhart, Bernhard A1 - Kleiner, Jan A1 - Endisch, Christian A1 - Kennel, Ralph T1 - Online Detection of Soft Internal Short Circuits in Lithium-Ion Battery Packs by Data-Driven Cell Voltage Monitoring T2 - Proceedings of the Energy Conversion Congress & Exposition - Asia, 2021 (ECCE-Asia 2021) UR - https://doi.org/10.1109/ECCE-Asia49820.2021.9479175 KW - Internal Short Circuit KW - Fault Diagnosis KW - Lithium-Ion Battery KW - Battery Safety KW - Electric Vehicle Y1 - 2021 UR - https://doi.org/10.1109/ECCE-Asia49820.2021.9479175 SN - 978-1-7281-6344-4 SN - 2150-6086 SP - 1711 EP - 1718 PB - IEEE CY - Piscataway (NJ) ER - TY - JOUR A1 - Lechermann, Lorenz A1 - Kleiner, Jan A1 - Komsiyska, Lidiya A1 - Hinterberger, Michael A1 - Endisch, Christian T1 - A comparative study of data-driven electro-thermal models for reconfigurable lithium-ion batteries in real-time applications JF - Journal of Energy Storage UR - https://doi.org/10.1016/j.est.2023.107188 Y1 - 2023 UR - https://doi.org/10.1016/j.est.2023.107188 SN - 2352-1538 SN - 2352-152X VL - 2023 IS - 65 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Kleiner, Jan A1 - Stuckenberger, Magdalena A1 - Komsiyska, Lidiya A1 - Endisch, Christian T1 - Real-time core temperature prediction of prismatic automotive lithium-ion battery cells based on artificial neural networks JF - Journal of Energy Storage UR - https://doi.org/10.1016/j.est.2021.102588 KW - Lithium-ion battery KW - Battery modeling KW - Electro-thermal model KW - Thermal model KW - Neural network KW - Real-time application Y1 - 2021 UR - https://doi.org/10.1016/j.est.2021.102588 SN - 2352-1538 VL - 2021 IS - 39 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Kleiner, Jan A1 - Heider, Alexander A1 - Komsiyska, Lidiya A1 - Elger, Gordon A1 - Endisch, Christian T1 - Thermal behavior of intelligent automotive lithium-ion batteries: Experimental study with switchable cells and reconfigurable modules JF - Journal of Energy Storage UR - https://doi.org/10.1016/j.est.2021.103274 KW - lithium-ion KW - intelligent battery KW - switchable cell KW - reconfiguration KW - smart cell KW - thermal management Y1 - 2021 UR - https://doi.org/10.1016/j.est.2021.103274 SN - 2352-1538 VL - 2021 IS - 44, Part A PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Schmid, Michael A1 - Kleiner, Jan A1 - Endisch, Christian T1 - Early detection of Internal Short Circuits in series-connected battery packs based on nonlinear process monitoring JF - Journal of Energy Storage UR - https://doi.org/10.1016/j.est.2021.103732 KW - Lithium-ion battery KW - Internal Short Circuit KW - Fault diagnosis KW - Kernel Principal Component Analysis KW - Cell inconsistencies KW - Battery safety Y1 - 2022 UR - https://doi.org/10.1016/j.est.2021.103732 SN - 2352-1538 VL - 2022 IS - 48 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Kleiner, Jan A1 - Lechermann, Lorenz A1 - Komsiyska, Lidiya A1 - Elger, Gordon A1 - Endisch, Christian T1 - Thermal behavior of intelligent automotive lithium-ion batteries BT - operating strategies for adaptive thermal balancing by reconfiguration JF - Journal of energy storage UR - https://doi.org/10.1016/j.est.2021.102686 KW - intelligent battery KW - thermal management KW - balancing KW - cell-to-cell variations KW - inhomogeneities KW - thermal modeling Y1 - 2021 UR - https://doi.org/10.1016/j.est.2021.102686 SN - 2352-1538 VL - 2021 IS - 40 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Schwertner, Stefan A1 - Buchberger, Tobias A1 - Diehl, Simon A1 - Ferg, Rebekka A1 - Hanzl, Christian A1 - Hartmann, Christoph A1 - Hölzle, Markus A1 - Kleiner, Jan A1 - Komsiyska, Lidiya A1 - Lewerenz, Meinert A1 - Liebhart, Bernhard A1 - Schmid, Michael A1 - Schneider, Dominik A1 - Scholz, Florian A1 - Speer, Sascha A1 - Stöttner, Julia A1 - Terbrack, Christoph A1 - Hinterberger, Michael A1 - Endisch, Christian T1 - Design and Implementation of an Intelligent Reconfigurable High-Voltage Battery System for Next-Generation Electric Vehicles JF - Batteries N2 - Battery system engineers face the challenge of balancing competing requirements regarding performance, maintainability, sustainability, safety, and cost—especially in the automotive industry. IBS potentially offer a solution with fewer trade-offs. They feature a battery management system with advanced sensing and data analysis capabilities that facilitate improved battery monitoring and operation. Reconfigurable energy storage units enable sophisticated operating strategies, including complete cell state control, full energy content utilization, and a measured response to faults. This article presents the design, development, and operation of a full-scale intelligent battery system prototype comprising 324 automotive lithium-ion cells with a nominal voltage of 400V. The system exhibits a modular single cell architecture and an advanced centralized battery management system. We detail the system architecture, hardware and software component design, and system integration. Initial tests demonstrate the battery’s operability, extended functionality, and enhanced safety. Our analysis shows that the additional losses introduced by reconfigurability are more than offset by the benefits of full energy utilization—even for new cells, with increasing advantage as aging progresses. The results underscore the potential of intelligent battery systems and motivate further research and development toward economic assessment and industrial adoption. UR - https://doi.org/10.3390/batteries11110424 Y1 - 2025 UR - https://doi.org/10.3390/batteries11110424 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-64086 SN - 2313-0105 VL - 11 IS - 11 PB - MDPI CY - Basel ER -