{Accurate|Correct|Precise} forecasting of lithium-ion battery {performance|overall performance|efficiency|functionality} {is important|is essential|is very important|is vital|is significant} for easing {consumer|customer} {concerns|issues} {about the|concerning the|regarding the|in regards to the} {safety|security} and reliability of electric {vehicles|automobiles|autos|cars}. Most {research|study|analysis|investigation} on battery {health|well being|wellness|overall health} prognostics focuses {on the|around the} R&D setting where cells are subjected {to the|towards the|for the} same usage patterns, yet in practice there is great variability in use across cells and cycles, making forecasting much more challenging. Here, we address this challenge by combining electrochemical impedance spectroscopy (EIS), a non-invasive measurement of battery state, with probabilistic machine learning. We generated a dataset of 40 commercial lithium-ion coin cells cycled under multistage constant current charging/discharging, with currents randomly changed between cycles to emulate realistic use patterns. We show that future discharge capacities can be predicted with calibrated uncertainties, given the future cycling protocol and a single EIS measurement made just before charging, and without any knowledge of usage history. Our method is data-efficient, requiring just eight cells to achieve a test error of less than 10%, and robust to dataset shifts. Our model can forecast {well|nicely|effectively|properly} into the future, attaining a test error of less than 10% when projecting 32 cycles ahead. Further, we find that model {performance|overall performance|efficiency|functionality} can be boosted by 25% by augmenting EIS with additional features derived from historical capacity-voltage curves. Our results suggest that battery {health|well being|wellness|overall health} is better quantified by a multidimensional vector rather than a scalar State of {Health|Well being|Wellness|Overall health}, thus deriving informative electrochemical `biomarkers’ in tandem with machine learning is key to predictive battery management and control. Formula of Methyl 6-cyanonicotinate Formula of Ir[dF(F)ppy]2(dtbbpy)PF6 PMID:24576999
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