Reproducible long-term cycling data of Al2O3 coated LiNi0.70Co0.15Mn0.15O2 cathodes for lithium-ion batteries

Abstract

LiNixCoyMn1-x-yO2 (NCM) based cathodes for Li-ion batteries (LIBs) are of great interest due to their higher energy density and lower costs compared to conventional LiCoO2 based cathodes. However, NCM based cathodes suffer from instabilities of the cathode-electrolyte interface resulting in faster capacity fading during long-term cycling. Different NCM compositions along with different coatings have been developed to protect the interface. However, a detailed understanding why and how coatings work is still missing. Up to now, no state-of-the-art NCM or coating material have been agreed upon yet, making it difficult to benchmark the performance of the coating material. Undefined standards complicate the use of experimentally produced data for model-based studies, which are a key element in assessing the beneficial effect of coatings. In this work, we therefore describe reproducible long-term cycling data of NCM based cathodes with and without an Al2O3 based coating. The data set is provided to be used for parameter fitting and/or as training data to encourage the simulation of the performance of LIBs in model-based approaches.

State of Charge-Dependent Impedance Spectroscopy as a Helpful Tool to Identify Reasons for Fast Capacity Fading in All-Solid-State Batteries

“…Li­(Ni 0.70 Co 0.15 Mn 0.15 )­O 2 (Gelon LIB) was coated by a wet chemical coating approach to improve the surface stability of NCM in contact with the SSE as recently discussed in detail in our previous work. , In brief, 0.279 mL of a trimethylaluminum solution (2 M in toluene, Sigma-Aldrich) was added to 15 mL of dried toluene (Sigma-Aldrich) and stirred for 1 h. After adding 2 g of Li­(Ni 0.70 Co 0.15 Mn 0.15 )­O 2 , the suspension was stirred for 12 h, filtered, washed with 20 mL of toluene, and dried at 200 °C. The obtained powder was divided into two batches.…”

Advances of LiCoO2 in Cathode of Aqueous Lithium‐Ion Batteries

“…As more data is generated and computational power increases, machine learning algorithms will become even more powerful in predicting material properties and guiding the design of new materials. [ 224,225 ]…”

“…As more data is generated and computational power increases, machine learning algorithms will become even more powerful in predicting material properties and guiding the design of new materials. [224,225] In conclusion, machine learning has the potential to revolutionize the field of materials research for aqueous lithium-ion batteries. By leveraging the power of data analysis and prediction, machine learning can assist in the selection of electrode materials and the design of current collectors, ultimately leading to the development of more efficient and stable batteries.…”

Challenges and opportunities using Ni-rich layered oxide cathodes in Li-ion rechargeable batteries: the case of nickel cobalt manganese oxides

“…In comparison, the uncoated (NCM) electrode retained only 56.60% of its capacity (102.5 mA h g À1 at 1C after 100 cycles). 231 Oxides such as Al 2 O 3 , ZrO 2 , Li 2 O-2B 2 O 3 (LBO), and Li 2 MnO 3 are also used as coating materials [232][233][234] apart from utilizing carbon-derived materials such as graphene oxides and polymers. The schematic of the TiO 2 coating procedure is shown in Fig.…”