Accurate battery lifetime prediction is important for preventative maintenance, warranties, and improved cell design and manufacturing. However, manufacturing variability and usage-dependent degradation make life prediction challenging. Here, we investigate new features derived from capacity-voltage data in early life to predict the lifetime of cells cycled under widely varying charge rates, discharge rates, and depths of discharge. Features were extracted from regularly scheduled reference performance tests (i.e., low rate full cycles) during cycling. The early-life features capture a cell's state of health and the rate of change of component-level degradation modes, some of which correlate strongly with cell lifetime. Using a newly generated dataset from 225 nickel-manganese-cobalt/graphite Li-ion cells aged under a wide range of conditions, we demonstrate a lifetime prediction of in-distribution cells with 15.1% mean absolute percentage error using no more than the first 15% of data, for most cells. Further testing using a hierarchical Bayesian regression model shows improved performance on extrapolation, achieving 21.8% mean absolute percentage error for out-of-distribution cells. Our approach highlights the importance of using domain knowledge of lithium-ion battery degradation modes to inform feature engineering. Further, we provide the community with a new publicly available battery aging dataset with cells cycled beyond 80% of their rated capacity.

Accurate prediction of battery health is essential for real-world system management and lab-based experiment design. However, building a life-prediction model from different cycling conditions is still a challenge. Large lifetime variability results from both cycling conditions and initial manufacturing variability, and this -- along with the limited experimental resources usually available for each cycling condition -- makes data-driven lifetime prediction challenging. Here, a hierarchical Bayesian linear model is proposed for battery life prediction, combining both individual cell features (reflecting manufacturing variability) with population-wide features (reflecting the impact of cycling conditions on the population average). The individual features were collected from the first 100 cycles of data, which is around 5-10% of lifetime. The model is able to predict end of life with a root mean square error of 3.2 days and mean absolute percentage error of 8.6%, measured through 5-fold cross-validation, overperforming the baseline (non-hierarchical) model by around 12-13%.

Metal–air batteries, consisting of a metal anode and an air cathode, have been attracted significant interest by the research community as energy storage devices, because of their high energy density (in particular, compared to lithium ion batteries -LIBs-). A wide diversity of active metals can be used as anode material such as Li, Ca, Mg, Al, Fe, and Zn. However, they have so far found their use only in very particular markets requiring high energy density such as hearing aids. Indeed, despite very significant experimental research efforts, recharging them electrochemically constitute a significant challenge, that if unlocked, will pave the way to a wider diversity of ZAB applications such as Electric Vehicles. Reversing this process to recharge electrochemically a ZAB would imply a heterogeneous deposition of Zn in the anode and the formation of dendrites that can short-circuit the cell, similarly to what can happen in lithium metal batteries.