(Invited) Additives Approach to Improve the Performance of LiNi0.5Mn1.5O2 5V Cathodes

Abstract

The high voltage spinel cathode of LiNi0.5Mn1.5O2 has an average working voltage of 4.7 V, high-rate performance with faster diffusion properties, and is viewed as one of the promising cathode materials for high energy lithium-ion batteries. The lack of Co in such cathode materials is also advantageous due to the issues of artisan mining and geopolitical tensions associated with Co mining. However, the high working voltages in LiNi0.5Mn1.5O2 exceeds the stability of electrolyte window and the redox center in the cathode materials is mainly Ni2+ while Mn stays at the 4+ charge state. Due to the dominance of the Mn elements in the cathode, acid leaching from the impurity of the electrolytes can cause Mn dissolution, and the degradation of LNMO//Gr is associated with transition metal dissolution and crosstalk to the anode side, causing loss of inventory Li loss. For example, the decomposition of ethylene carbonate leads to formation of glycolic acid and difluorophosphoric acid (HPO2F2).Using additives in the electrolyte formulation is an economic way of improving the performance without significant change in either the processing industry or manufacturing technology, and here we present the approach of using a combinatorial approach to optimize the additives for LNMO//Gr full cells. The degradation mechanism will also be discussed. Machine learning (ML) method has been used effectively to predict molecular structures, preferred quantity, as well as formation of interfaces.1-3 In this study, we used ML to formulate the high-performance additives with both improved capacity retention and reduced initial and final impedance. Okamoto, Y.; Kubo, Y., Ab Initio Calculations of the Redox Potentials of Additives for Lithium-Ion Batteries and Their Prediction through Machine Learning. ACS Omega 2018, 3 (7), 7868-7874.Bhowmik, A.; Castelli, I. E.; Garcia-Lastra, J. M.; Jørgensen, P. B.; Winther, O.; Vegge, T., A perspective on inverse design of battery interphases using multi-scale modelling, experiments and generative deep learning. Energy Storage Materials 2019, 21, 446-456.Hildenbrand, F.; Aupperle, F.; Stahl, G.; Figgmeier, E.; Sauer, D. U., Selection of Electrolyte Additive Quantities for Lithium-Ion Batteries Using Bayesian Optimization. Batteries & Supercaps 2022, 5 (7), e202200038.