Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
143 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

To Balance or to Not? Battery Aging-Aware Active Cell Balancing for Electric Vehicles (2401.03124v1)

Published 6 Jan 2024 in eess.SY and cs.SY

Abstract: Due to manufacturing variabilities and temperature gradients within an electric vehicle's battery pack, the capacities of cells in it decrease differently over time. This reduces the usable capacity of the battery - the charge levels of one or more cells might be at the minimum threshold while most of the other cells have residual charge. Active cell balancing (i.e., transferring charge among cells) can equalize their charge levels, thereby increasing the battery pack's usable capacity. But performing balancing means additional charge transfer, which can result in energy loss and cell aging, akin to memory aging in storage technologies due to writing. This paper studies when cell balancing should be optimally triggered to minimize aging while maintaining the necessary driving capability. In particular, we propose optimization strategies for cell balancing while minimizing their impact on aging. By borrowing terminology from the storage domain, we refer to this as "wear leveling-aware" active balancing.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (34)
  1. S. Chakraborty et al., “Embedded systems and software challenges in electric vehicles,” in Design, Automation & Test in Europe Conference (DATE), 2012.
  2. G. Georgakos et al., “Reliability challenges for electric vehicles: from devices to architecture and systems software,” in 50th Annual Design Automation Conference (DAC), 2013.
  3. S. Park, L. Zhang, and S. Chakraborty, “Battery assignment and scheduling for drone delivery businesses,” in International Symposium on Low Power Electronics and Design (ISLPED), 2017.
  4. T. Baumhöfer et al., “Production caused variation in capacity aging trend and correlation to initial cell performance,” Journal of Power Sources, vol. 247, pp. 332–338, Feb. 2014.
  5. M. Kauer, S. Narayanaswamy, S. Steinhorst, and S. Chakraborty, “Rapid analysis of active cell balancing circuits,” IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., vol. 36, no. 4, pp. 694–698, 2017.
  6. S. Narayanaswamy, S. Park, S. Steinhorst, and S. Chakraborty, “Multi-pattern active cell balancing architecture and equalization strategy for battery packs,” in International Symposium on Low Power Electronics and Design (ISLPED), Jul. 2018.
  7. S. Narayanaswamy, S. Steinhorst, M. Lukasiewycz, M. Kauer, and S. Chakraborty, “Optimal dimensioning and control of active cell balancing architectures,” IEEE Trans. Veh. Technol., vol. 68, no. 10, pp. 9632–9646, 2019.
  8. V. J. Ovejas and A. Cuadras, “State of charge dependency of the overvoltage generated in commercial Li-ion cells,” Journal of Power Sources, vol. 418, pp. 176–185, Apr. 2019.
  9. D. Roy, S. Narayanaswamy, A. Probstl, and S. Chakraborty, “Multi-stage optimization for energy-efficient active cell balancing in battery packs,” in IEEE/ACM International Conference on Computer-Aided Design (ICCAD), IEEE/ACM.   IEEE, Nov. 2019, pp. 1–8.
  10. M. Kauer, S. Narayanaswamy, S. Steinhorst, M. Lukasiewycz, and S. Chakraborty, “Many-to-many active cell balancing strategy design,” in 20th Asia and South Pacific Design Automation Conference (ASP-DAC), 2015.
  11. M. Kauer, S. Narayanaswamy, M. Lukasiewycz, S. Steinhorst, and S. Chakraborty, “Inductor optimization for active cell balancing using geometric programming,” in Design, Automation & Test in Europe Conference & Exhibition (DATE), 2015.
  12. S. Narayanaswamy, S. Park, S. Steinhorst, and S. Chakraborty, “Design automation for battery systems,” in International Conference on Computer-Aided Design (ICCAD), 2018.
  13. S. Steinhorst, M. Kauer, A. Meeuw, S. Narayanaswamy, M. Lukasiewycz, and S. Chakraborty, “Cyber-physical co-simulation framework for smart cells in scalable battery packs,” ACM Trans. Design Autom. Electr. Syst., vol. 21, no. 4, pp. 62:1–62:26, 2016.
  14. S. Steinhorst et al., “Distributed reconfigurable battery system management architectures,” in 21st Asia and South Pacific Design Automation Conference (ASP-DAC), 2016.
  15. S. Narayanaswamy, S. Steinhorst, M. Lukasiewycz, M. Kauer, and S. Chakraborty, “Optimal dimensioning of active cell balancing architectures,” in Design, Automation & Test in Europe Conference & Exhibition, (DATE), 2014.
  16. S. Chakraborty et al., “Automotive cyber-physical systems: A tutorial introduction,” IEEE Des. Test, vol. 33, no. 4, pp. 92–108, 2016.
  17. W. Chang and S. Chakraborty, “Resource-aware automotive control systems design: A cyber-physical systems approach,” Found. Trends Electron. Des. Autom., vol. 10, no. 4, pp. 249–369, 2016.
  18. N. Peters et al., “Web browser workload characterization for power management on HMP platforms,” in 11th International Conference on Hardware/Software Codesign and System Synthesis (CODES), 2016.
  19. Y. Gu and S. Chakraborty, “A hybrid DVS scheme for interactive 3d games,” in 14th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), 2008.
  20. ——, “Power management of interactive 3d games using frame structures,” in 21st International Conference on VLSI Design, 2008.
  21. J. Wang et al., “Degradation of lithium ion batteries employing graphite negatives and nickel-cobalt-manganese oxide+spinel manganese oxide positives: Part 1, aging mechanisms and life estimation,” Journal of Power Sources, vol. 269, pp. 937–948, Dec. 2014.
  22. X. Jin et al., “Physically-based reduced-order capacity loss model for graphite anodes in Li-ion battery cells,” Journal of Power Sources, vol. 342, pp. 750–761, Feb. 2017.
  23. A. C. Baughman and M. Ferdowsi, “Double-Tiered Switched-Capacitor Battery Charge Equalization Technique,” IEEE Transactions on Industrial Electronics, vol. 55, no. 6, pp. 2277–2285, Jun. 2008.
  24. M. Kauer et al., “Modular system-level architecture for concurrent cell balancing,” in 50th Annual Design Automation Conference (DAC), 2013.
  25. S. Narayanaswamy, M. Kauer, S. Steinhorst, M. Lukasiewycz, and S. Chakraborty, “Modular active charge balancing for scalable battery packs,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 25, no. 3, pp. 974–987, Mar. 2017.
  26. D. Roy et al., “Optimal scheduling for active cell balancing,” in IEEE Real-Time Systems Symposium (RTSS), 2019.
  27. A. Lamprecht et al., “Enhancing battery pack capacity utilization in electric vehicle fleets via SoC-preconditioning,” in 22nd Euromicro Conference on Digital System Design (DSD), 2019.
  28. P. Kremer et al., “Active cell balancing for life cycle extension of Lithium-ion batteries under thermal gradient,” in International Symposium on Low Power Electronics and Design (ISLPED), 2021.
  29. A. Pröbstl et al., “SOH-aware active cell balancing strategy for high power battery packs,” in Design, Automation & Test in Europe, 2018.
  30. F. Yang et al., “A study of the relationship between coulombic efficiency and capacity degradation of commercial lithium-ion batteries,” Energy, vol. 145, pp. 486–495, 2018.
  31. B. Balagopal and M.-Y. Chow, “The state of the art approaches to estimate the state of health (SOH) and state of function (SOF) of lithium Ion batteries,” in 13th Intl. Conf. Industrial Informatics (INDIN), 2015.
  32. IBM, “ILOG CPLEX Optimization Studio version: 12.9.0,” 2019. [Online]. Available: https://www.ibm.com/docs/en/icos/12.9.0
  33. M. Dubarry, N. Vuillaume, and B. Y. Liaw, “From single cell model to battery pack simulation for Li-ion batteries,” Journal of Power Sources, vol. 186, no. 2, pp. 500–507, Jan. 2009.
  34. D. J. Watts and S. H. Strogatz, “Collective dynamics of ‘small-world’ networks,” Nature, vol. 393, no. 6684, pp. 440–442, Jun. 1998.

Summary

We haven't generated a summary for this paper yet.