Linear Approximation of Cyclic Battery Aging Costs for MILP-Based Power Dispatch Optimization

Energy storage technologies are essential to decouple the production and consumption of electric energy in terms of time. The rapidly rising penetration of volatile renewable energy sources and growing market shares of electric vehicles increase the demand for efficient batteries with state of the art management systems. For elaborate applications with (predictive) power dispatch optimization at runtime, e.g. complex microgrids with participation in energy markets and ancillary services, reliable models and fast algorithms are crucial. To develop these, mathematical models capturing the dynamics of battery behavior over time are needed. This paper proposes a linear approximation approach for nonlinear cyclic aging dynamics of batteries that can easily be integrated into existing dispatch optimization software based on Mixed-Integer Linear Programming.

[1]  E. Sarasketa-Zabala,et al.  Realistic lifetime prediction approach for Li-ion batteries , 2016 .

[2]  Zita Vale,et al.  Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming , 2010 .

[3]  M. Wohlfahrt‐Mehrens,et al.  Temperature dependent ageing mechanisms in Lithium-ion batteries – A Post-Mortem study , 2014 .

[4]  Gan Ning,et al.  Capacity fade study of lithium-ion batteries cycled at high discharge rates , 2003 .

[5]  D. Sauer,et al.  Calendar and cycle life study of Li(NiMnCo)O2-based 18650 lithium-ion batteries , 2014 .

[6]  Cristian Bovo,et al.  Optimal operational planning for PV-Wind-Diesel-battery microgrid , 2015, 2015 IEEE Eindhoven PowerTech.

[7]  Benjamin Müller,et al.  The SCIP Optimization Suite 3.2 , 2016 .

[8]  M. Broussely,et al.  Aging mechanism in Li ion cells and calendar life predictions , 2001 .

[9]  Erlon Cristian Finardi,et al.  A mixed integer linear programming model for the energy management problem of microgrids , 2015 .

[10]  N. Ramakrishnan,et al.  Cyclic Capacity Fade Plots for aging studies of Li-ion cells , 2013 .

[11]  G. Lutzemberger,et al.  Cycle life evaluation of lithium cells subjected to micro-cycles , 2015, 2015 5th International Youth Conference on Energy (IYCE).

[12]  Benjamin Müller,et al.  The SCIP Optimization Suite 5.0 , 2017, 2112.08872.

[13]  Y. Dube,et al.  Low temperature aging tests for lithium-ion batteries , 2015, 2015 IEEE 24th International Symposium on Industrial Electronics (ISIE).

[14]  Kit Po Wong,et al.  Efficient real-time residential energy management through MILP based rolling horizon optimization , 2015, 2015 IEEE Power & Energy Society General Meeting.

[15]  Ghanim Putrus,et al.  The effect of cycling on the state of health of the electric vehicle battery , 2013, 2013 48th International Universities' Power Engineering Conference (UPEC).

[16]  Herbert L Case,et al.  An accelerated calendar and cycle life study of Li-ion cells. , 2001 .

[17]  Delphine Riu,et al.  A review on lithium-ion battery ageing mechanisms and estimations for automotive applications , 2013 .

[18]  N. M. Muhamad Razali,et al.  Profit-based optimal generation scheduling of a microgrid , 2010, 2010 4th International Power Engineering and Optimization Conference (PEOCO).

[19]  I. Bloom,et al.  Calendar and PHEV cycle life aging of high-energy, lithium-ion cells containing blended spinel and layered-oxide cathodes , 2011 .

[20]  Herbert L Case,et al.  Correlation of Arrhenius behaviors in power and capacity fades with cell impedance and heat generation in cylindrical lithium-ion cells , 2003 .

[21]  Andreas Jossen,et al.  Cycle life characterisation of large format lithium-ion cells , 2013, 2013 World Electric Vehicle Symposium and Exhibition (EVS27).