(Invited) Analyzing and Minimizing Capacity Fade through Model Predictive Control - Theory and Experimental Validation

Abstract

A lithium-ion battery loses its capacity with cycle life.1 This capacity loss or fade is due to various mechanisms which are largely due to unwanted side reactions, taking place in these batteries. Some of these mechanisms include the loss of active material in SEI layer growth, loss of lithium in plating side reactions, irreversible phase change in solid particles, electrolyte decomposition at high temperatures, along with mechanical degradation or breaking of particles due to intercalation induced stresses.2,3 The contribution of each of those factors depends on the cell chemistry and the operating conditions. The collective effects of these individual fade mechanisms are still being investigated. Some of these fade mechanisms are potentially unsafe to the batteries, for eg. the plating side reaction could lead to dendrite formation which could internally short the battery. This motivates the need to be able to manage and use the batteries more efficiently in order to enhance the cycle life of the batteries. Physics-based model predictive control (MPC) is one of the ways to achieve that.4-6 In our past work,7-8 using dynamic optimization strategies, we have developed the optimal charging protocols to minimize the capacity fade due to SEI-layer formation, due to lithium-plating and intercalation-induced stresses, while controlling internal temperatures inside the batteries, using physics-based reformulated 1D and 2D models.9-10 In order to experimentally show and quantify the benefits of the MPC approach, the tests were conducted at NREL on an actual 16Ah NMC based prismatic Kokam cell. The SEI-layer based fade mechanism was chosen as it reasonably mimicked the actual fade of the cells. The optimal profiles based on minimizing SEI layer formation was chosen and the cells were cycled using those profiles. Fig. 1 shows the experimental results for capacity of the battery vs cycle life for a CC-CV charge/5C discharge compared to MPC charge/5C discharge. We have shown that using such an approach could lead to as much as 20% increase in the usable capacity of the batteries or an increase in cycle life by up to 40%. Acknowledgements The work presented herein was funded in part by the Advanced Research Projects Agency – Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000275 along with the Clean Energy Institute (CEI) at the University of Washington (UW) and the Washington Research Foundation. References J. Newman, K. E. Thomas, H. Hafezi, and D. R. Wheeler, J. Electrochem. Soc., 150, A176 (2003).P. Arora, R.E.White, and M. Doyle, Electrochem. Soc., 145(10), 3647 (1998).V. Ramadesigan, P. W. C. Northrop, S. De, S. Santhanagopalan, R. D. Braatz, and V. R. Subramanian, J. Electrochem. Soc., 159(3), R31 (2012).M. Doyle, T. F. Fuller and J. Newman, J. Electrochem. Soc., 140, 1526 (1993).T. F. Fuller, M. Doyle and J. Newman, J. Electrochem. Soc., 141, 1 (1994). M. D. Canon, C. D. Cullum, and E. Polak, Theory of Optimal Control and Mathematical Programming, McGraw-Hill, New York (1969).B. Suthar, V. Ramadesigan, S. De, R. D. Braatz and V. R. Subramanian, Phy.Chem.Chem. Phy., 16(1), 277 (2014). B. Suthar, P. W.C. Northrop, R. D. Braatz and V. R. Subramanian, J. Electrochem. Soc., 161(11), F3144 (2014).P. W. C. Northrop, V. Ramadesigan, S. De, and V. R. Subramanian, J. Electrochem. Soc., 158(12), A1461 (2011).P. W. C. Northrop, M. Pathak, D. Rife, S. De, S. Santhanagopalan and V. R. Subramanian, J. Electrochem. Soc, 162(6), A940 (2015). Figure 1