Engineering lithium nickel cobalt manganese oxides cathodes: A computational and experimental approach to bridging gaps

桥接(联网) 可扩展性 纳米技术 储能 电池(电) 计算机科学 材料科学 工艺工程 工程类 功率(物理) 物理 计算机网络 量子力学 数据库
作者
Anand Rajkamal,Ankur Sharma,Bhargav Krishna Pullagura,Ranjit Thapa,Hern Kim
出处
期刊:Chemical Engineering Journal [Elsevier]
卷期号:481: 148223-148223 被引量:1
标识
DOI:10.1016/j.cej.2023.148223
摘要

Lithium-ion batteries (LIBs) have transformed our envisioned future into a reality where induction motor engines power electric vehicles (EVs). While LIBs offer impressive advantages compared to other energy storage systems for EVs, they face practical deployment challenges in performance, cost, and scalability. One critical component of LIBs that has garnered significant attention is the cathode, primarily due to its high cost, stemming from expensive cobalt metals and limited capacity, which cannot meet the current demand. However, layered lithium nickel cobalt manganese oxide (NCM) materials have achieved remarkable market success. Despite their potential, much current research focuses on experimental or theoretical aspects, leaving a gap that needs bridging. Understanding the surface chemistry of these oxides and conducting operando observations is crucial. Combining advanced surface analysis techniques with theoretical calculations (viz., quantum mechanics) is proposed to bridge this knowledge gap. This review delves into recent performance achievements (viz., projected driving performance, current EVs model, and battery specifications), challenges, and opportunities associated with various NCM materials as cathode materials. It also explores cutting-edge developments in experimental and theoretical techniques that analyze battery operations, address frontier challenges, and provide novel insights. Furthermore, the review comprehensively discusses the concept of single-crystal (SC) NCM and its practical implications in EVs. Finally, the review provides an outlook on future guidelines for designing NCM cathodes for LIBs, emphasizing the convergence of experimental and computational/theoretical approaches to achieve superior performance.
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