Sebastian Steinhorst
Matthias Kauer
Arne Meeuw
Swaminathan Narayanaswamy
Samarjit Chakraborty
Abstract
This article introduces a Cyber-physical Co-Simulation Framework (CPCSF) for design and analysis of smart cells that enable scalable battery pack and Battery Management System (BMS) architectures. In contrast to conventional cells in battery packs, where all cells are monitored and controlled centrally, each smart cell is equipped with its own electronics in the form of a Cell Management Unit (CMU). The CMU maintains the cell in a safe and healthy operating state, while system-level battery management functions are performed by cooperation of the smart cells via communication. Here, the smart cells collaborate in a self-organizing fashion without a central controller instance. This enables maximum scalability and modularity, significantly simplifying integration of battery packs. However, for this emerging architecture, system-level design methodologies and tools have not been investigated yet. By contrast, components are developed individually and then manually tested in a hardware development platform. Consequently, the systematic design of the hardware/software architecture of smart cells requires a cyber-physical multi-level co-simulation of the network of smart cells that has to include all the components from the software, electronic, electric, and electrochemical domains. This comprises distributed BMS algorithms running on the CMUs, the communication network, control circuitry, cell balancing hardware, and battery cell behavior. For this purpose, we introduce a CPCSF that enables rapid design and analysis of smart cell hardware/software architectures. Our framework is then applied to investigate request-driven active cell balancing strategies that make use of the decentralized system architecture. In an exhaustive analysis on a realistic 21.6kW h Electric Vehicle (EV) battery pack containing 96 smart cells in series, the CPCSF is able to simulate hundreds of balancing runs together with all system characteristics, using the proposed request-driven balancing strategies at highest accuracy within an overall time frame of several hours. Consequently, the presented CPCSF for the first time allows us to quantitatively and qualitatively analyze the behavior of smart cell architectures for real-world applications.
- F. Baronti, G. Fantechi, R. Roncella, and R. Saletti. 2012. Intelligent cell gauge for a hierarchical battery management system. In IEEE Transportation Electrification Conference and Expo (ITEC). 1--5. DOI:http://dx.doi.org/10.1109/ITEC.2012.6243471 Google Scholar
Cross Ref
- F. Baronti, G. Fantechi, R. Roncella, and R. Saletti. 2013. High-efficiency digitally controlled charge equalizer for series-connected cells based on switching converter and super-capacitor. IEEE Trans. Industr. Inform. 9, 2 (2013), 1139--1147. DOI:http://dx.doi.org/10.1109/TII.2012.2223479 Google ScholarCross Ref
- F. Baronti, C. Bernardeschi, L. Cassano, A. Domenici, R. Roncella, and R. Saletti. 2014a. Design and safety verification of a distributed charge equalizer for modular li-ion batteries. IEEE Trans. Industr. Inform. 10, 2 (2014), 1003--1011. DOI:http://dx.doi.org/10.1109/TII.2014.2299236 Google ScholarCross Ref
- F. Baronti, R. Roncella, and R. Saletti. 2014b. Performance comparison of active balancing techniques for lithium-ion batteries. J. Power Sources 267, 1 (2014), 603--609. DOI:http://dx.doi.org/10.1016/j.jpowsour.2014.05.007 Google ScholarCross Ref
- A. C. Baughman and M. Ferdowsi. 2008. Double-tiered switched-capacitor battery charge equalization technique. IEEE Trans. Industr. Electron. 55, 6 (June 2008), 2277--2285. DOI:10.1109/TIE.2008.918401 Google ScholarCross Ref
- Bosch. 1991. Controller Area Network, Version 2.0b. Retrieved from http://www.can.bosch.com/.Google Scholar
- M. Brandl, H. Gall, M. Wenger, V. Lorentz, M. Giegerich, F. Baronti, G. Fantechi, L. Fanucci, R. Roncella, R. Saletti, S. Saponara, A. Thaler, M. Cifrain, and W. Prochazka. 2012. Batteries and battery management systems for electric vehicles. In Proc. of Design, Automation Test in Europe Conference Exhibition (DATE). 971--976. DOI:http://dx.doi.org/10.1109/DATE.2012.6176637 Google ScholarDigital Library
- J. Cao, N. Schofield, and A. Emadi. 2008. Battery balancing methods: A comprehensive review. In Proc. of Vehicle Power and Propulsion Conference (VPPC). 1--6. DOI:http://dx.doi.org/10.1109/VPPC.2008.4677669 Google ScholarCross Ref
- M. Caspar, T. Eiler, and S. Hohmann. 2014. Comparison of active battery balancing systems. In Proc. of Vehicle Power and Propulsion Conference (VPPC). 1--8. DOI:http://dx.doi.org/10.1109/VPPC.2014.7007027 Google ScholarCross Ref
- M. Caspar and S. Hohmann. 2014. Optimal cell balancing with model-based cascade control by duty cycle adaption. In IFAC World Congress, Vol. 19. 10311--10318. DOI:http://dx.doi.org/10.3182/20140824-6-ZA-1003.01613 Google ScholarCross Ref
- C. Danielson, F. Borrelli, D. Oliver, D. Anderson, and T. Phillips. 2013. Constrained flow control in storage networks: Capacity maximization and balancing. Automatica 49, 9 (2013), 2612--2621. DOI:http://dx.doi.org/10.1016/j.automatica.2013.05.014 Google ScholarDigital Library
- M. Daowd, N. Omar, P. Van Den Bossche, and Joeri Van Mierlo. 2011. Passive and active battery balancing comparison based on MATLAB simulation. In Proc. of Vehicle Power and Propulsion Conference (VPPC). 1--7. DOI:http://dx.doi.org/10.1109/VPPC.2011.6043010 Google ScholarCross Ref
- R. Erickson and D. Maksimovic. 2001. Fundamentals of Power Electronics. Springer, Berlin. Google ScholarCross Ref
- J. Guerin and W. Liu. 2010. Cell balancing algorithm verification through a simulation model for lithium ion energy storage systems. SAE Publication 2010-01-1079 (2010). DOI:http://dx.doi.org/ 10.4271/2010-01-1079Google Scholar
- L. Hua, S. Sambamoorthy, S. Shukla, J. Thorp, and L. Mili. 2011. Power system and communication network co-simulation for smart grid applications. In Proc. of Innovative Smart Grid Technologies (ISGT). 1--6. DOI:http://dx.doi.org/10.1109/ISGT.2011.5759166 Google ScholarCross Ref
- M. Kauer, S. Narayanaswami, S. Steinhorst, M. Lukasiewycz, S. Chakraborty, and L. Hedrich. 2013. Modular system-level architecture for concurrent cell balancing. In Proc. of the Design Automation Conference (DAC). 155:1--155:10. DOI:http://dx.doi.org/10.1145/2463209.2488926 Google ScholarDigital Library
- M. Kauer, S. Narayanaswamy, M. Lukasiewycz, S. Steinhorst, and S. Chakraborty. 2015a. Inductor optimization for active cell balancing using geometric programming. In Proc. of the Conference on Design, Automation and Test in Europe (DATE). 1--4. DOI:http://dx.doi.org/10.7873/DATE.2015.0051 Google ScholarDigital Library
- M. Kauer, S. Narayanaswamy, S. Steinhorst, M. Lukasiewycz, and S. Chakraborty. 2015b. Many-to-many active cell balancing strategy design. In Proc. of the Asia and South Pacific Design Automation Conference (ASP-DAC). 267--272. DOI:http://dx.doi.org/10.1109/ASPDAC.2015.7059016 Google ScholarCross Ref
- N. Kutkut. 1998. A modular nondissipative current diverter for EV battery charge equalization. In Proc. of Applied Power Electronics Conference and Exposition (APEC), Vol. 2. 686--6902. DOI:http://dx.doi.org/10.1109/APEC.1998.653973 Google ScholarCross Ref
- N. Kutkut and D. Divan. 1996. Dynamic equalization techniques for series battery stacks. In Proc. of the International Telecommunications Energy Conference (INTELEC). 514--521. DOI:http://dx.doi.org/ 10.1109/INTLEC.1996.573384 Google ScholarCross Ref
- Y. Lee and M. Cheng. 2005. Intelligent control battery equalization for series connected lithium-ion battery strings. IEEE Trans. Indust. Electron. 52, 5 (Oct. 2005), 1297--1307. DOI:http://dx.doi.org/10.1109/TIE.2005.855673 Google ScholarCross Ref
- J. Liu, Z. Huang, J. Peng, and J. Wang. 2015. Distributed cooperative voltage equalization for series-connected super-capacitors. In Proc. of the American Control Conference (ACC). 4523--4528. DOI:http://dx.doi.org/10.1109/ACC.2015.7172041 Google ScholarCross Ref
- L. Lu, X. Han, J. Li, J. Hua, and M. Ouyang. 2013. A review on the key issues for lithium-ion battery management in electric vehicles. J. Power Sources 226, 0 (2013), 272--288. DOI:http://dx.doi.org/ 10.1016/j.jpowsour.2012.10.060 Google ScholarCross Ref
- M. Lukasiewycz, S. Steinhorst, and S. Narayanaswamy. 2014. Verification of balancing architectures for modular batteries. In Proc. of the International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS). 30:1--30:10. DOI:http://dx.doi.org/10.1145/2656075.2656104 Google ScholarDigital Library
- S. Moore and P. Schneider. 2001. A review of cell equalization methods for lithium ion and lithium polymer battery systems. SAE Publication 2001-01-0959 (2001). DOI:http://dx.doi.org/10.4271/2001-01-0959 Google ScholarCross Ref
- S. Narayanaswamy, S. Steinhorst, M. Lukasiewycz, M. Kauer, and S. Chakraborty. 2014. Optimal dimensioning of active cell balancing architectures. In Proc. of Design, Automation Test in Europe Conference Exhibition (DATE). 140:1--140:6. DOI:http://dx.doi.org/10.7873/DATE.2014.153 Google ScholarDigital Library
- A. Otto, S. Rzepka, T. Mager, B. Michel, C. Lanciotti, T. Günther, and O. Kanoun. 2012. Battery management network for fully electrical vehicles featuring smart systems at cell and pack level. In Advanced Microsystems for Automotive Applications 2012, Gereon Meyer (Ed.). Springer, Berlin. DOI:http://dx.doi.org/10.1007/978-3-642-29673-4_1 Google ScholarCross Ref
- M. Petricca, D. Shin, A. Bocca, A. Macii, E. Macii, and M. Poncino. 2013. An automated framework for generating variable-accuracy battery models from datasheet information. In Proc. of the International Symposium on Low Power Electronics and Design (ISLPED). 365--370. DOI:http://dx.doi.org/10.1109/ISLPED.2013.6629324 Google ScholarDigital Library
- M. Preindl, C. Danielson, and F. Borrelli. 2013. Performance evaluation of battery balancing hardware. In Proc. of the European Control Conference (ECC). 4065--4070.Google Scholar
- S. Steinhorst, M. Lukasiewycz, S. Narayanaswamy, M. Kauer, and S. Chakraborty. 2014. Smart cells for embedded battery management. In Proc. of the International Conference on Cyber-Physical Systems, Networks, and Applications (CPSNA). 59--64. DOI:http://dx.doi.org/10.1109/CPSNA.2014.22 Google ScholarDigital Library
- T. Stuart and W. Zhu. 2009. Fast equalization for large lithium ion batteries. IEEE Aerospace Electron. Syst. Mag. 24, 7 (July 2009), 27--31. DOI:http://dx.doi.org/10.1109/MAES.2009.5208557 Google ScholarCross Ref
- Team SimPy. 2015. SimPy Discrete Event Simulation Library for Python. Retrieved from http://simpy.readthedocs.org/.Google Scholar
Index Terms
- Cyber-Physical Co-Simulation Framework for Smart Cells in Scalable Battery Packs
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