Health-aware Control for Health Management of Lithium-ion Battery in a V2G Scenario

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Jun 27, 2024
Monica Spinola Felix John J. Martinez-Molina Christophe Bérenguer Chetan S. Kulkarni Marcos E. Orchard

Abstract

In response to the urgent need to combat climate change and reduce greenhouse gas emissions, the transition towards renewable energy sources such as solar and wind power is indispensable. However, the intermittent nature of these sources poses significant challenges to the stability of power grids. Battery Energy Storage Systems (BESS) offer a viable solution, and there is potential for Electric Vehicles (EVs) to serve as energy reservoirs, thereby bolstering grid stability through Vehicle-to-Grid (V2G) technology. While V2G holds promise, concerns persist regarding the longevity of batteries, particularly with the additional demand from charging and discharging cycles. To address these concerns, this study introduces a health-aware control strategy for V2G service scenarios. By employing feedback control mechanisms to adjust degradation rates, the strategy aims to effectively manage battery aging. Simulation outcomes of a V2G scenario with random input sources illustrate the efficacy of this proposed approach, demonstrating its potential applicability in practical settings where battery health needs to be managed. In summary, this research contributes to the advancement of health-aware strategies for an interconnected grid where electric vehicles participate as energy sources, with a primary focus on optimizing battery health management while fulfilling grid demands. Future efforts will concentrate on refining optimization strategies and integrating control methodologies with state estimators to ensure the performance of the approach on embedded battery health management systems.

How to Cite

Spinola Felix, M., Martinez-Molina, J. J. ., Bérenguer, C. ., Kulkarni, C. S. ., & Orchard, M. E. . (2024). Health-aware Control for Health Management of Lithium-ion Battery in a V2G Scenario. PHM Society European Conference, 8(1), 10. https://doi.org/10.36001/phme.2024.v8i1.4086
Abstract 9 | PDF Downloads 7

##plugins.themes.bootstrap3.article.details##

Keywords

Health-aware Control, Health Management, Lithium-ion Battery, BESS, Electrical Vehicles, Vehicle-to-Grid, Robust Control

References
Barr ́e, A., Deguilhem, B., Grolleau, S., G ́erard, M., Suard, F., & Riu, D. (2013). A review on lithium-ion battery ageing mechanisms and estimations for automotive applications. Journal of power sources, 241, 680–689.
Brown, D. W., Georgoulas, G., Bole, B., Pei, H.-L., Orchard, M., Tang, L., Vachtsevanos, G. (2009). Prognostics enhanced reconfigurable control of electro-mechanical actuators. In Annual conference of the phm society (Vol. 1).
Collath, N., Tepe, B., Englberger, S., Jossen, A., & Hesse, H. (2022). Aging aware operation of lithium-ion battery energy storage systems: A review. Journal of Energy Storage, 55, 105634.
Didier, B., Thierry, P., Sebastien, M., Christian, N., Severine, J. S. L., Bloch, D., & Severine, J. S. L. (2021). Li-ion batteries : development and perspectives / coordinated
by didier bloch, s´ebastien martinet, thierry priem, [et al.] ; [préface de séverine jouanneau si larbi]. Les Ulis: Science press, EDP sciences.
Fricke, K., Nascimento, R., Corbetta, M., Kulkarni, C., & Viana, F. (2023). An accelerated life testing dataset for lithium-ion batteries with constant and variable loading conditions. International Journal of Prognostics and Health Management, 14(2).
Félix, M. S., Martinez, J. J., & B´erenguer, C. (2023). Astate-space approach for remaining useful life control. IFAC-PapersOnLine, 56(2), 7728–7733.
Kipchirchir, E., Do, M. H., Njiri, J. G., & Soffker, D. (2023). Prognostics-based adaptive control strategy for lifetime control of wind turbines. Wind Energy Science, 8(4),
575–588.
Martinez, J. J., Félix, M. S., Kulkarni, C., Orchard, M., & Bérenguer, C. (2024). A novel dynamical model for diagnosis, prognosis and health-aware control of lithium-
ion batteries. Proceedings of the 12nd IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, to appear.
Pelletier, S., Jabali, O., Laporte, G., & Veneroni, M. (2017). Battery degradation and behaviour for electric vehicles: Review and numerical analyses of several mod-
els. Transportation Research Part B: Methodological,103, 158–187.
Reniers, J. M., Mulder, G., Ober-Blobaum, S., & Howey, D. A. (2018). Improving optimal control of grid connected lithium-ion batteries through more accurate battery and degradation modelling. Journal of Power Sources, 379, 91–102.
Section
Technical Papers