Charge–discharge properties of LiMn2O4-group positive electrode active materials for lithium-ion batteries using high-throughput experimental screening and machine learning models

Abstract

To improve the charge – discharge properties of an LiMn2O4 positive electrode active material for a lithium-ion battery, the effect of additive elements was investigated using high-throughput experiments and materials informatics techniques. First, the material libraries of LiMn1.4NixAyBzO4±δ (A, B = Mo, Ir, Bi, Eu, Zn, Y, Ce, and Ru, x + y + z = 0.6, x, y, z = 0, 0.2, 0.4, 0.6) were synthesized by the ink-jet technique, and the properties were estimated using X-ray diffraction and X-ray absorption near-edge structure (XANES) spectroscopy at SPring-8. Appropriate additives were searched for by machine learning models using composition-based explanatory and experimentally obtained objective variables without completing the lithium-ion battery cell. Next, LiMn2O4 specimens containing the additives were synthesized by the solid-state reaction method, and then the charge – discharge properties were verified using the sandwich-type electrochemical cell. Based on the results, LiMn1.6Ni0.2Ir0.1Mo0.1O4±δ, LiMn1.6Ni0.2Pd0.1W0.1O4±δ, LiMn1.6Ni0.2Ir0.1W0.1O4±δ, LiMn1.6Ni0.3W0.1O4±δ, and LiMn1.6Ni0.2Ru0.1W0.1O4±δ had approximately 10% larger current capacity and approximately 0.1 V higher average charge – discharge potential than LiMn2O4 without additives. The charge compensation of lithiation and delithiation could be caused by the valence change of Mn (Mn4+ ⇌ Mn3+) and Ni ions (Ni3+ ⇌ Ni2+), which was estimated by XANES spectroscopy.

Keywords

• high-throughput experiments
• materials informatics
• machine learning
• lithium-ion battery
• xanes
• positive electrode active material
• limn2o4
• chemical delithiation
• support vector regression