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 - 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 - CHAP A1 - Schneider, Dominik A1 - Endisch, Christian T1 - Robustness and reliability of model-based sensor data fusion in a lithium-ion battery system T2 - IEEE CCTA 2020, 4th IEEE Conference on Control Technology and Applications UR - https://doi.org/10.1109/CCTA41146.2020.9206336 KW - Batteries KW - Robustness KW - Parameter estimation KW - Current measurement KW - Noise measurement KW - Data models KW - Kalman filters Y1 - 2020 UR - https://doi.org/10.1109/CCTA41146.2020.9206336 SN - 978-1-7281-7140-1 SP - 685 EP - 691 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Schneider, Dominik A1 - Liebhart, Bernhard A1 - Endisch, Christian T1 - Adaptive Variance Estimation of Sensor Noise within a Sensor Data Fusion Framework T2 - 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC 2021) Proceedings UR - https://doi.org/10.1109/I2MTC50364.2021.9459790 Y1 - 2021 UR - https://doi.org/10.1109/I2MTC50364.2021.9459790 SN - 978-1-7281-9539-1 SN - 2642-2077 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Theiler, Michael A1 - Schneider, Dominik A1 - Endisch, Christian T1 - Kalman Filter Tuning Using Multi-Objective Genetic Algorithm for State and Parameter Estimation of Lithium-Ion Cells JF - Batteries N2 - To ensure a reliable and safe operation of battery systems in various applications, the system’s internal states must be observed with high accuracy. Hereby, the Kalman filter is a frequently used and well-known tool to estimate the states and model parameters of a lithium-ion cell. A strong requirement is the selection of a suitable model and a reasonable initialization, otherwise the algorithm’s estimation might be insufficient. Especially the process noise parametrization poses a difficult task, since it is an abstract parameter and often optimized by an arbitrary trial-and-error principle. In this work, a traceable procedure based on the genetic algorithm is introduced to determine the process noise offline considering the estimation error and filter consistency. Hereby, the parameters found are independent of the researcher’s experience. Results are validated with a simulative and experimental study, using an NCA/graphite lithium-ion cell. After the transient phase, the estimation error of the state-of-charge is lower than 0.6% and for internal resistance smaller than 4mΩ while the corresponding estimated covariances fit the error well. UR - https://doi.org/10.3390/batteries8090104 KW - battery model KW - Kalman filter KW - joint estimation KW - Kalman filter tuning KW - genetic algorithm KW - multi-objective optimization Y1 - 2022 UR - https://doi.org/10.3390/batteries8090104 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-29528 SN - 2313-0105 VL - 8 IS - 9 PB - MDPI CY - Basel ER - TY - CHAP A1 - Liebhart, Bernhard A1 - Diehl, Simon A1 - Schneider, Dominik A1 - Endisch, Christian A1 - Kennel, Ralph T1 - Enhancing the Cell Impedance Estimation of a Lithium-Ion Battery System with Embedded Power Path Switches T2 - APEC 2021: Thirty-Sixth Annual IEEE Applied Power Electronics Conference and Exposition UR - https://doi.org/10.1109/APEC42165.2021.9487173 KW - Reconfigurable Battery System KW - Impedance Spectroscopy KW - Power MOSFET KW - Pseudorandom Binary Sequence Y1 - 2021 UR - https://doi.org/10.1109/APEC42165.2021.9487173 SN - 978-1-7281-8949-9 SN - 2470-6647 SP - 967 EP - 974 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Schneider, Dominik A1 - Liebhart, Bernhard A1 - Endisch, Christian A1 - Kennel, Ralph T1 - Enhanced state and parameter estimation within reconfigurable battery systems for electric vehicles T2 - 2022 12th International Conference on Power, Energy and Electrical Engineering (CPEEE) UR - https://doi.org/10.1109/CPEEE54404.2022.9738700 KW - state estimation KW - battery management system KW - reconfigurable battery systems KW - power electronics Y1 - 2022 UR - https://doi.org/10.1109/CPEEE54404.2022.9738700 SN - 978-1-6654-2049-5 SP - 71 EP - 77 PB - IEEE CY - Piscataway, NJ ER - TY - JOUR A1 - Theiler, Michael A1 - Schneider, Dominik A1 - Endisch, Christian T1 - Experimental Investigation of State and Parameter Estimation within Reconfigurable Battery Systems JF - Batteries N2 - The battery system is one of the most-important, but also -critical components in the electric power-train. The battery’s system states and parameters are commonly tracked by the battery monitoring system. However, in reality, the accuracy of the state and parameter estimation may suffer from insufficient excitation of the system. Since the current states and parameters serve as the basis for many battery management system functions, this might lead to incorrect operation and severe damage. Reconfigurable battery systems allow enhancing the system’s excitation by applying a switching operation. In this contribution, the state and parameter estimation of a reconfigurable battery module were simulated and tested experimentally. Thereby, a low-exciting and a high-exciting drive cycle were compared. Furthermore, the switching patterns were applied to enhance the excitation and, hence, improve the estimation of an extended Kalman filter. The cells were switched via a pulse-width modulation signal, and the influence of frequency and duty cycle variation on the estimation accuracy were investigated. Compared to the low-excitation input, a significant improvement in the estimation of up to 46% for the state of charge and 78% for the internal resistance were achieved. Hereby, low frequencies and duty cycles proved to be particularly advantageous. Switching, however, has only a limited influence on an already highly excited system and may lead to additional aging due to higher heat generation. UR - https://doi.org/10.3390/batteries9030145 KW - battery model KW - Kalman filter KW - joint estimation KW - reconfigurable battery systems KW - state estimation KW - parameter estimation Y1 - 2023 UR - https://doi.org/10.3390/batteries9030145 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-31110 SN - 2313-0105 VL - 9 IS - 3 PB - MDPI CY - Basel ER - TY - JOUR A1 - Schneider, Dominik A1 - Liebhart, Bernhard A1 - Endisch, Christian T1 - Active state and parameter estimation as part of intelligent battery systems JF - Journal of Energy Storage UR - https://doi.org/10.1016/j.est.2021.102638 Y1 - 2021 UR - https://doi.org/10.1016/j.est.2021.102638 SN - 2352-1538 VL - 2021 IS - 39 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 -