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Ni-rich oxide cathode active materials are today key components in Li-ion batteries used for electric vehicles (EV). The high capacity of LiNixCoyMnzO2 and LiNixCoyAlzO2 (NCA) with x≥0.6 has enabled a continued increase in energy density and decrease in cost at the pack level, enabling the rapid growth of the EV market in recent years. Despite a wide use, rather fast material ageing results in the batteries being a source of considerable environmental impact [1]. Various degradation sources are highlighted in the literature, these include transition metal dissolution, surface layer reconstruction, particle cracking, etc [2]. However, when extrapolating the knowledge from a model cell to a commercial battery the relevance and the degree of degradation impact can drastically change [3]. The importance of approaching real conditions is a key to a full understanding of the ageing processes occurring in commercial cells. In the given work, we analyse commercial state-of-art 2170 cylindrical cells. The cells were aged at various temperatures and state of charge windows, targeting divergent usage scenarios. The degradation processes were accessed with a wide range of analytical techniques applied to extracted electrodes (x-ray diffraction, inductively coupled plasma atomic emission spectroscopy, scanning electron microscopy, energy-dispersive X-ray spectroscopy, cycling voltammetry, incremental capacity analysis, electrochemical impedance spectroscopy and intermittent current interruption). In the current work we show that depending on the operating conditions, influence of ageing sources varies. Additionally, ageing mapping within cell geometry is presented highlighting “hot spots” of the cell [4]. References [1] W. Liu et al., “Nickel-Rich Layered Lithium Transition-Metal Oxide for High-Energy Lithium-Ion Batteries,” Angew. Chemie Int. Ed., vol. 54, no. 15, pp. 4440–4457, Apr. 2015. [2] T. Li, X.-Z. Yuan, L. Zhang, D. Song, K. Shi, and C. Bock, Degradation Mechanisms and Mitigation Strategies of Nickel-Rich NMC-Based Lithium-Ion Batteries, vol. 2018, no. January 2017. Springer Singapore, 2019. [3] M. Lucu et al., “Data-driven nonparametric Li-ion battery ageing model aiming at learning from real operation data – Part A: Storage operation,” J. Energy Storage, vol. 30, p. 101409, Aug. 2020. [4] Mikheenkova et al., in manuscript. |
