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Energy Storage - Equalization of Cycle Lifetime

The concept of Flexible Distribution of EneRgy and Storage resources (FDERS) was introduced in [1],[2]. It has been shown recently in [3] that FDERS can extend the operation of an islanded industrial microgrid by as much as 80%. FDERS transforms the fixed electrical power network into a flexible one for achieving potential savings. It was inspired by the survival mechanisms found in ecological species that cooperatively team up in flexible formations for extending their endurance limits while facing extremely challenging conditions. Examples of such flexible and cooperative formations are displayed in Fig. 1, viz., the V-shape formation of a bird flock [4],[5] and peloton formation of a cycling racing team [6]-[8] - where a periodic rotation of their positions helps in reinvigorating all the team members during long distance travel.

The initial exploratory research for FDERS was published in several papers (8 conference papers [1],[3],[10]-[15], 2 accepted journal papers [2],[9], and 3 manuscripts under review for IEEE Trans. on Industry Applications). Prior work on FDERS application to an islanded microgrid supplying an extremely harsh load such as cement plant crusher-conveyor load at an industrial site has offered the following advantages [3]:

(1)   An 80% extended period of the islanded industrial microgrid operation, and

(2)   Equalization of battery cycle lifetime

Fig. 2 illustrates selected results from an in-depth analysis conducted on an islanded microgrid consisting of four fuel cell-battery DERs that are supplying a large and fluctuating crusher-conveyor load at the cement plant. An active cycling approach for achieving battery cycle lifetime equalization and 80% extended operation of the microgrid was presented in [3]. Prior results of applying three kinds of FDERS passive cycling strategies for battery lifetime balancing have been published in [9]. A comparison of all the results obtained from multiple FDERS approaches is shown in Table 1. A brief description of these results is given below.

As seen in Fig. 2, each DER consists of a Solid Oxide Fuel Cell (SOFC) along with a Li-ion battery stack (A123 Systems model #ANR26650M1A) [16]. If four unequally rated (i.e., 30 kW, 60 kW, 90 kW and 120 kW) DERs are distributed in the microgrid and not physically located right next to each other (i.e., physical reactances X1o != X2o != X3o != X4o; for instance, X1o < X2o < X3o < X4o), then their individual responses are found to differ for the 300 kW pulsed load profile crusher-conveyor load [3]. It was also observed that differences in the transient responses resulted in significant variations between the utilization of the Li-ion batteries of each DER unit. The more leading or ‘electrically’ closer a DER unit is to the load, the more stress is placed on its battery. This caused differences in battery State of Charge (SoC) as well as temperature. The detailed analysis using the Li-ion battery aging (MATLAB) model [17] that has been validated against other hybrid electric vehicle applications (for which, the experimental data was available) [18], it was observed that the SoC and temperature are two key factors that affect the rate of aging of the battery [17],[19]-[25]. Selected results of the battery life analysis under ‘status quo’ conditions are illustrated in the middle portion of Fig. 2, and it is quite evident that in such a fixed formation of four DERs significant variations in accumulated battery age occur after several repeated load cycles. As such, Batt 1 (i.e., nearest to the load) reached End of Life (EoL) after 11627 load cycles leading to a system shutdown, although Batt 2, Batt 3, and Batt 4 had not reached their EoL. 


An exhaustive investigation has been carried out in applying FDERS [9],[3] to better understand if there is any advantage in rotating the ‘electrical’ positions of four DERs periodically - analogous to the periodic rotation of positions among members of migratory bird flock or cycling racing team - to help in reinvigorating all the team members. These analyses resulted in the development of four kinds of battery cycling approaches as outcomes that are displayed in Table 1, with the plots corresponding to best outcome (i.e., Approach D from [3]) shown at the bottom of Fig. 2. As seen in Table 1 and Fig. 2, the Approach D lowered the average SoC and temperature substantially, which resulted in equalization of their battery life as well as State of Health (SoH) [26],[27]. This approach was successful in the achievement of all the four batteries to age at almost the exact same rate and reach EoL after ~21000 pulsed load cycles (i.e., 80% extended operation of the microgrid).

This was made possible in FDERS by means of synthesizing a virtual reactance (Xk-add) in each DER’s controller by modifying its three-phase reference voltage vector. Then, the effective interface reactance of the kth DER (i.e., Xk = Xko + Xk-add) can be practically varied to accomplish in-situ reconfiguration in ‘electrical’ positions of the four DERs [11]. The pecking order of the formation of DERs within the microgrid can be effortlessly controlled with the help of this virtual reactance. Additionally, if the ‘virtual inertia’ is increased within the active power/frequency droop controls of the leading DER, the pecking order of the DER formation within the islanded microgrid can be extended for a longer period [2].


[1]   M. S. Illindala, “Flexible Distribution of Energy and Storage Resources,” 2012 IEEE Energy Conversion Congress and Exposition (ECCE), 2012, pp.4069-4076, 15-20 Sept. 2012.

[2]   M. S. Illindala, H. Khasawneh*, A. Renjit*, “Flexible Distribution of Energy and Storage Resources: Integrating These Resources into a Microgrid,” IEEE Industry Applications Magazine, Vol. 21, No.5, Sept. 2015.

[3]   H. J. Khasawneh*, M. S. Illindala, “Equalization of Battery Cycle Life Through Flexible Distribution of Energy and Storage Resources,” 2014 IEEE I&CPS Technical Conference, 2014, pp. 1-9.

[4]   P. B. S. Lissaman, C. A. Schollenberger, “Formation Flight of Birds,” Science, Vol. 168, 1970, pp. 1003-1005. doi:10.1126/science.168.3934.1003.

[5]   H. Weimerskirch, J. Martin, Y. Clerquin, P. Alexandre, S. Jiraskova, “Energy Saving in Flight Formation,” Nature, Vol. 413, 2001, pp. 697-698. doi:10.1038/35099670.

[6]   C. R. Kyle, “Reduction of wind resistance and power output of racing cyclists and runners traveling in groups,” Ergonomics, 1979, Vol. 22: pp. 387-397.

[7]   A. G. Edwards, W. C. Byrnes, “Aerodynamic characteristics as determinants of the drafting effect in cycling,” Journal of Medical Science Sports Exercise, Jan 2007, Vol. 39, No. 1, pp. 170-176.

[8]   J. Brisswalter, C. Hausswirth, “Consequences of Drafting on Human Locomotion: Benefits on Sports Performance,” International Journal of Sports Physiology and Performance, Apr 2008, Vol. 3, No. 1, pp. 3-15.

[9]   H. Khasawneh*, M. Illindala, “Battery Life Balancing in a Microgrid through Flexible Distribution of Energy and Storage Resources,” Journal of Power Sources, vol. 261, Sep. 2014, pp. 378-388.

[10]  H. Khasawneh*, M. Illindala, “Quantitative and Qualitative Evaluation of Flexible Distribution of Energy and Storage Resources,” 2013 IEEE Energy Conversion Congress and Exposition (ECCE), Sep. 15-19, 2013.

[11]  A. A. Renjit*, M. Illindala, “In-situ Reconfiguration for Flexible Distribution of Energy and Storage Resources,” 2013 IEEE Energy Conversion Congress and Exposition (ECCE), Sep. 15-19, 2013.

[12]  M. Haj-ahmed*, M. S. Illindala, “Investigation of Protection Schemes for Flexible Distribution of Energy and Storage Resources,” 2014 IEEE I&CPS Technical Conference, 2014, pp. 1-9.

[13]  H. J. Khasawneh*, M. S. Illindala, “State-of-Health Based Load Sharing Strategy in Vehicle-to-Grid Systems,” 2014 IEEE Transportation Electrification Conference (ITEC), 2014, pp. 1-6.

[14]  A. A. Renjit*, M. S. Illindala, R. Yedavalli, “Stability Robustness Analysis and its Improvement for an Industrial Microgrid,” 2014 IEEE IAS Annual Meeting, 2014, pp. 1-9.

[15]  H. J. Khasawneh*, M. S. Illindala, “Supercapacitor Cycle Life Equalization in a Microgrid Through Flexible Distribution of Energy and Storage Resources,” 2014 IEEE IAS Annual Meeting, 2014, pp. 1-9.

[16]  A123 Systems, Data sheet MD1000001-02 for high power lithium ion battery cell ANR26650M1. Available online at:

[17]  A. Millner, “Modeling Lithium Ion battery degradation in electric vehicles,” 2010 IEEE Conference on Innovative Technologies for an Efficient and Reliable Electricity Supply (CITRES), 2010, pp. 349-356.

[18]  S. Peterson, J. Apt, J. Whitacre, “Lithium Ion Battery Cell Degradation Resulting from Realistic Vehicle and Vehicle- to Grid Utilization,” Journal of Power Sources, Volume 195, 2010, pp. 2385-2392.

[19]  S. Santhanagopalan, Q. Zhang, K. Kumaresan, R.E. White, "Parameter Estimation and Life Modeling of Lithium-Ion Cells," Journal of The Electrochemical Society, Vol. 155, No. 4, 2008, pp. A345-A353.

[20]  M. Broussely, S. Herreyre, P. Biensan, P. Kasztejna, K. Nechev, R.J. Staniewicz, “Aging mechanism in Li ion cells and calendar life predictions,” Journal of Power Sources, Vol. 97, 2001, pp. 13-21.

[21]  M. Broussely, Ph. Biensan, F. Bonhomme, Ph. Blanchard, S. Herreyre, K. Nechev, R.J. Staniewicz, “Main aging mechanisms in Li ion batteries,” Journal of Power Sources, Vol. 146, No. 1, 2005, pp. 90-99.

[22]  J. Vetter, P. Novák, M. R. Wagner, C. Veit, K. C. Möller, J. O. Besenhard, M. Winter, M. Wohlfahrt-Mehrens, C. Vogler, A. Hammouche, “Ageing mechanisms in lithium-ion batteries,” Journal of Power Sources, Vol. 147, No. 1, 2005, pp. 269-281.

[23]  G. Ning, R. E. White, B. N. Popov, “A Generalized Cycle Life Model of Rechargeable Li-ion Batteries,” Electrochimica Acta., Vol. 51, 2006, pp. 2012-2022.

[24]  G. Ning, B. Haran, B. N. Popov, “Capacity Fade Study of Lithium ion Batteries Cycled at High Discharge Rates,” Journal of Power Sources, Vol. 117, 2003, pp. 160-169.

[25]  D. P. Abraham, J. L. Knuth, D. W. Dees, I. Bloom, J. P. Chrisophersen, “Performance Degradation of High-Power Lithium-ion cells-Electrochemistry of Harvested Electrodes,” Journal of Power Sources, Vol. 170, No. 2 ,2007, pp. 465-475.

[26]  C. Guenther, B. Schott, W. Hennings, P. Waldowski, M. A. Danzer, “Model-based investigation of electric vehicle battery aging by means of vehicle-to-grid scenario simulations,” Journal of Power Sources, Vol. 239, 2013, pp. 604-610.

[27]  P. Ramadass, B. Haran, R. White, B. N. Popov, “Mathematical modeling of the capacity fade of Li-ion cells,” Journal of Power Sources, Vol. 123, No. 2, 2003, Pages 230-240.