Lithium batteries are exciting next generation energy storage as lithium anodes have the most negative potential of any known material, which enables lithium anodes to have a high energy density and high discharge voltage. However, lithium batteries suffer from many issues that have prevented their widespread commercialization as rechargeable cells. When lithium metal batteries are cycled, the anode is used non-uniformly which leads to the development of finger-like dendrites. Dendrite formation leads to cell short circuits, higher rates of adverse reactions, and large volume changes. These problems are major safety issues but also account for the low Coulombic efficiency and poor cycling performance2. The utilization of all-solid-state cells for lithium batteries can reduce the growth of dendrites3, which greatly improves safety2. However, solid-state batteries suffer from high resistances at ambient temperatures.
Wu et al.4 studied an all-solid-state battery composed of a lithium foil anode and a cathode made of lithium ferric phosphate (LFP) particles in a block copolymer matrix. In the cathode, the electronic conduction is facilitated by a conducting polymer, poly(3-hexylthiophene) (P3HT). The conductivity of this semiconductor polymer depends on the electrode potential because the conducting polymer is oxidized and reduced when transporting the electrons.
The electronic conductivity of the cathode polymer matrix without the LFP active particles was measured as a function of the voltage, and this experimental fit was used with a macro-homogeneous continuum model developed by Newman and associates5,6. The model output was relatively independent of the rate and failed to capture the trend of the experimental data. The authors then reduced the electronic conductivity until the best agreement between the experimental data and the model was achieved4.
This talk explores the possibility of obtaining better fits for the model predictions by performing detailed estimation of different parameters that affect the performance of the system by combining different/robust simulation and estimation methods.
Acknowledgments
The authors are thankful for the financial support from the Battery 500 Consortium and the Clean Energy Institute (CEI) at the University of Washington.
References
- Takada, K., AIP Conf. Proc., 1765, (2016).
- Cheng, X. B., Zhang, R., Zhao, C. Z. & Zhang, Q., Chem. Rev., 117, 10403–10473 (2017).
- Kim, J. G. et al., J. Power Sources, 282, 299–322 (2015).
- Wu, S.-L., Javier, A. E., Devaux, D., Balsara, N. P. & Srinivasan, V. J., Electrochem. Soc., 161, A1836–A1843 (2014).
- Newman, J. S. & Tobias, C. W., J. Electrochem. Soc., 109, 1183 (1962).
- Doyle, M., Fuller, T. F. & Newman, J., J. Electrochem. Soc., 140, 1526 (1993).