Expanding the lifetime of Li-ion batteries through optimization of charging profiles

Abstract The increasing penetration of renewable energy sources, incentivized by government policies and decreasing costs, brings the challenge of making electricity supply and consumption meet, as these energy sources are intermittent by nature. Li-ion battery systems integrated in the grid will provide much needed ancillary services, such as frequency regulation, peak shaving and load levelling, so that the challenges of future cleaner grids can be properly addressed. Therefore, efforts on all fronts are being made to further reduce the price of operation, materials and production of Li-ion battery systems. In this research, we propose an optimization of the conventional CC-CV charging protocol typically used in Li-ion batteries. The proposed protocol aims at expanding the lifetime of the system. A reduced-order electrochemical model for a Li-ion cell is used to simulate its dynamic behavior. Additionally, an optimization algorithm is developed and coupled to the dynamic model to obtain optimal charging-discharging profiles that maximizes the cell lifetime. A charging protocol that combines two different charging profiles proved to be an easily implementable and effective way of increasing the cell life time by ca. 27% in these simulations, in comparison to the standard CC-CV protocol.

[1]  Ralph E. White,et al.  Development of First Principles Capacity Fade Model for Li-Ion Cells , 2004 .

[2]  Yu Peng,et al.  On-line life cycle health assessment for lithium-ion battery in electric vehicles , 2018, Journal of Cleaner Production.

[3]  Venkatasailanathan Ramadesigan,et al.  Optimal charging profiles for mechanically constrained lithium-ion batteries. , 2014, Physical chemistry chemical physics : PCCP.

[4]  Dan M. Ionel,et al.  Modeling and Management of Batteries and Ultracapacitors for Renewable Energy Support in Electric Power Systems–An Overview , 2015 .

[5]  Yaqun He,et al.  Recovery of LiCoO2 and graphite from spent lithium-ion batteries by Fenton reagent-assisted flotation , 2017 .

[6]  Yi-Hsien Chiang,et al.  Development of a converterless energy management system for reusing automotive lithium-ion battery applied in smart-grid balancing , 2017 .

[7]  Yajuan Yu,et al.  Evaluation of lithium-ion batteries through the simultaneous consideration of environmental, economic and electrochemical performance indicators , 2018 .

[8]  Ralph E. White,et al.  Maximizing the Life of a Lithium-Ion Cell by Optimization of Charging Rates , 2010 .

[9]  B. Scrosati,et al.  Lithium batteries: Status, prospects and future , 2010 .

[10]  Andreas Jossen,et al.  Charging protocols for lithium-ion batteries and their impact on cycle life—An experimental study with different 18650 high-power cells , 2016 .

[11]  Venkat R. Subramanian,et al.  Generic Model Control for Lithium-Ion Batteries , 2017 .

[12]  Juntao Hu,et al.  Efficient and economical recovery of lithium, cobalt, nickel, manganese from cathode scrap of spent lithium-ion batteries , 2018, Journal of Cleaner Production.

[13]  B. Dunn,et al.  Electrical Energy Storage for the Grid: A Battery of Choices , 2011, Science.

[14]  Christoph H. Glock,et al.  Operating a storage-augmented hybrid microgrid considering battery aging costs , 2018, Journal of Cleaner Production.

[15]  Rolf Findeisen,et al.  Optimal charging strategies in lithium-ion battery , 2011, Proceedings of the 2011 American Control Conference.

[16]  F. Baronti,et al.  Battery Management System: An Overview of Its Application in the Smart Grid and Electric Vehicles , 2013, IEEE Industrial Electronics Magazine.

[17]  Lip Huat Saw,et al.  Integration issues of lithium-ion battery into electric vehicles battery pack , 2016 .

[18]  Ralph E. White,et al.  Review of Models for Predicting the Cycling Performance of Lithium Ion Batteries , 2006 .

[19]  Kaile Zhou,et al.  Multi-objective optimal dispatch of microgrid containing electric vehicles , 2017 .