We report on the first year of calendar ageing of commercial high‐energy 21700 lithium‐ion cells, varying over eight state of charge (SoC) and three temperature values. Lithium‐nickel‐cobalt‐aluminium oxide (NCA) and graphite with silicon suboxide (Gr‐SiO_x) form cathodes and anodes of those cells, respectively. Degradation is fastest for cells at 70–80 % SoC according to monthly electrochemical check‐up tests. Cells kept at 100 % SoC do not show the fastest capacity fade but develop internal short circuits for temperatures T≥40 °C. Degradation is slowest for cells stored close to 0 % SoC at all temperatures. Rates of capacity fade and their temperature dependencies are distinctly different for SoC values below and above 60 %, respectively. Differential voltage analyses, apparent activation energy analysis, and endpoint slippage tracking provide useful insights into the degradation mechanisms and the respective roles of anode and cathode potential. We discuss how reversible losses of lithium might play a role in alleviating the rate of irreversible losses on commercial cells.

We demonstrate the predictive power of a parametrised Doyle-Fuller-Newman (DFN) model of a commercial cylindrical (21700) lithium-ion cell with NCA/Gr-SiOx chemistry. Model parameters result from the deconstruction of a fresh commercial cell to determine/confirm chemistry and micro-structure, and also from electrochemical experiments with half-cells built from electrode samples. The simulations predict voltage profiles for (i) galvanostatic discharge and (ii) drive-cycles. Predicted voltage responses deviate from measured ones by <1% throughout at least ∼95% of a full galvanostatic discharge, whilst the drive cycle discharge is matched to a ∼1%–3% error throughout. All simulations are performed using the online computational tool DandeLiion, which rapidly solves the DFN model using only modest computational resources. The DFN results are used to quantify the irreversible energy losses occurring in the cell and deduce their location. In addition to demonstrating the predictive power of a properly validated DFN model, this work provides a novel simplified parametrisation workflow that can be used to accurately calibrate an electrochemical model of a cell.

The coupling among disparate time-scales is ubiquitous in many chemical and biological systems. We have recently investigated the effect of fast and, long-term, slow dynamics in surface processes underlying some electrocatalytic reactions. Herein we report on the effect of temperature on the coupled slow and fast dynamics of a model system, namely the electro-oxidation of formic acid on platinum studied at five temperatures between 5 and 45 °C. The main result was a turning point found at 25 °C, which clearly defines two regions for the temperature dependency on the overall kinetics. In addition, the long-term evolution allowed us to compare reaction steps related to fast and slow evolutions. Results were discussed in terms of the key role ofPtOspecies, which chemically couple slow and fast dynamics. In summary we were able to: (a) identify the competition between two reaction steps as responsible for the occurrence of two temperature domains; (b) compare the relative activation energies of these two steps; and (c) suggest the role of a given reaction step on the period-increasing set of reactions involved in the oscillatory dynamics. The introduced methodology could be applied to other systems to uncover the temperature dependence of complex chemical networks.

We report on a cycle ageing study of commercial NCA/Gr+Si cells, in which reversible capacity fluctuations turn a central experimental finding upside down: an upper voltage limit of 4.1 V seems to cause faster degradation than going all the way to 4.2 V. The underlying effect is the reversible loss of lithium inventory into passive anode overhang areas. We demonstrate how the resulting artefact arises from a combination of slow transport processes and the related time periods spent in specific state-of-charge regions. We propose an alternative visualisation tool to identify and manage such artefacts, often neglected in typical ageing studies.