A greyscale erosion algorithm for tomography (GREAT) to rapidly detect battery particle defects

Particle micro-cracking is a major source of performance loss within lithium-ion batteries, however early detection before full particle fracture is highly challenging, requiring time consuming high-resolution imaging with poor statistics. Here, various electrochemical cycling (e.g., voltage cut-off, cycle number, C-rate) has been conducted to study the degradation of Ni-rich NMC811 (LiNi0.8Mn0.1Co0.1O2) cathodes characterized using laboratory X-ray micro-computed tomography. An algorithm has been developed that calculates inter- and intra-particle density variations to produce integrity measurements for each secondary particle, individually. Hundreds of data points have been produced per electrochemical history from a relatively short period of characterization (ca. 1400 particles per day), an order of magnitude throughput improvement compared to conventional nano-scale analysis (ca. 130 particles per day). The particle integrity approximations correlated well with electrochemical capacity losses suggesting that the proposed algorithm permits the rapid detection of sub-particle defects with superior materials statistics not possible with conventional analysis.

A. Wade, T. M. M. Heenan, M. Kok, T. Tranter, A. Leach, C. Tan, R. Jervis, D. J. L. Brett, P. R. Shearing