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The Incremental Capacity (IC) is a rich source of data for the state‐of‐health estimation of lithium‐ion batteries. This data is typically collected during a low C‐rate (dis)charge of the battery which is not representative of many real‐world applications outside the research laboratories. Here, this limitation is showcased to be mitigated by employing a new feature‐extraction technique applied to a large dataset including 105 batteries with cycle lives ranging from 158 to 1637 cycles. The state‐of‐health of these batteries is successfully predicted with a mean‐absolute‐percentage error below 0.7 % by using three regression models of support vector regressor, multi‐layer perceptron, and random forest. The methodologies proposed in this work facilitate the development of accurate IC‐based state‐of‐health predictors for lithium‐ion batteries in on‐board applications.

This paper describes a surface flattening technique, which has been developed in particular to obtain a complete view of the cortical surface of the brain. However, the method is able to produce an overall planar view of any anatomical or real-life object, provided it is topologically compatible with the sphere (i.e. genus 0). It computes the shading of the original surface for rays casted from a nearby surrounding surface and unfolds this surface in a 2D plane, without introducing major distortions. The flat image consisting of the mapped shading results has the advantage that the sulci (i.e. the grooves characterizing the superficial brain geometry) of the cortical surface of the brain can be followed in their entirety, which facilitates the study and the recognition of their patterns.The new visualization method is integrated into a versatile medical image analysis environment. A first study to assess its usefulness has been accomplished and is also reported in this paper.

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