Li-ion batteries are a key technology for both electro-mobility and stationary energy storage systems. In order to be able to represent and improve their service life in these applications, a better understanding of the processes which lead to the degradation of the individual cells is essential. The work presented in this article focuses on the comparative post mortem analysis of type 18650 commercially available cells containing the state of the art active materials (Cathode: LiMn2O4 (LMO) and Li(Ni1/3Mn1/3Co1/3)O2 (NMC), Anode: Graphite). These cells were subjected to various different ageing procedures. Amongst other effects, the cells investigated revealed signs of crack formation in the LMO- and NMC-particles, a loss in the mechanical integrity of the cathode active mass and plastic deformation of cell structure together with pronounced delamination between the active mass layers, the separator and the current collector.
We investigate the use of learning approaches to handle Bayesian inverse problems in a computationally efficient way when the signals to be inverted present a moderately high number of dimensions and are in large number. We propose a tractable inverse regression approach which has the advantage to produce full probability distributions as approximations of the target posterior distributions. In addition to provide confidence indices on the predictions, these distributions allow a better exploration of inverse problems when multiple equivalent solutions exist. We then show how these distributions can be used for further refined predictions using importance sampling, while also providing a way to carry out uncertainty level estimation if necessary. The relevance of the proposed approach is illustrated both on simulated and real data in the context of a physical model inversion in planetary remote sensing. The approach shows interesting capabilities both in terms of computational efficiency and multimodal inference.
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