Development and Implementation of Lithium-Ion Battery Performance and Capacity Fade Models for Resource Planning Tools

The usefulness of renewable sources of energy in grid applications is dependent on the energy storage technology (i.e., battery type) and how the storage medium is utilized. Research is underway to develop resource planning tools that can select an optimum equipment set and operate the components in an energy-efficient manner.1-3 In this work, an optimization framework is used to select equipment sets including diesel generators, wind energy, solar energy, and batteries based on the location, mission size and duration. Such optimization problems fall in the category of mixed integer nonlinear programming (MINLP) models whose instances require lengthy solve times depending on the nonlinearity involved.4 To make the optimization problem computationally tractable, simplifying and other techniques (e.g., covexification) can be used to convert the original MINLP to a mixed integer linear program (MIP). Battery models, being severely nonlinear, pose significant challenges in such an optimization framework (MIP). Previous efforts in this direction were focused on using linear model for lithium-ion batteries (derived from available data on a commercial lithium-ion battery) to be used with other energy sources.5 While these linear models captured the essence of lithium-ion battery performance, they lacked the ability to capture the typical temperature-dependent performance and capacity degradation. Ignoring these effects may lead to under-sizing of the battery capacity. These linear models can be improved in two ways: 1) addition of temperature effects in the performance model of a lithium-ion battery and 2) incorporating capacity fade effects during the utilization of a battery. While incorporating these effects in the battery model adds nonlinearities, simplifying assumptions and convexification are used to circumvent computational difficulties. Addition of these two effects in the battery model will improve the fidelity of the model used in the optimization framework and will give rise to realistic prediction in a resource planning tool. With the improved battery model in the optimization framework, several scenarios with different loads and weather conditions are being studied to derive the best possible energy mix to efficiently and economically deliver power to the desired location. Acknowledgements The work presented herein was funded by the Office of Naval Research. References 1. T. Barbier, Optimisation de la stratégie et du dimensionnement des systèmes hybrides éoliens, diesel, batterie pour sites isolés, in, École Polytechnique de Montréal (2013). 2. H. E. LLC, sd http://www . homerenergy. com (2009). 3. U. Sureshkumar, P. Manoharan and A. Ramalakshmi, in Advances in Engineering, Science and Management (ICAESM), 2012 International Conference on, p. 94 (2012). 4. K. A. Pruitt, S. Leyffer, A. M. Newman and R. J. Braun, Optimization and Engineering, 15, 167 (2014). 5. Michael Scioletti, Johanna S Goodman, Alexandra M. Newman and S. Leyffer, Design and Dispatch of a Hybrid Power Generation System for Remote Locations, Submitted, (2015).

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