"Knees" in lithium-ion battery aging trajectories

Lithium-ion batteries can last many years but sometimes exhibit rapid, nonlinear degradation that severely limits battery lifetime. In this work, we review prior work on "knees" in lithium-ion battery aging trajectories. We first review definitions for knees and three classes of "internal state trajectories" (termed snowball, hidden, and threshold trajectories) that can cause a knee. We then discuss six knee "pathways", including lithium plating, electrode saturation, resistance growth, electrolyte and additive depletion, percolation-limited connectivity, and mechanical deformation -- some of which have internal state trajectories with signals that are electrochemically undetectable. We also identify key design and usage sensitivities for knees. Finally, we discuss challenges and opportunities for knee modeling and prediction. Our findings illustrate the complexity and subtlety of lithium-ion battery degradation and can aid both academic and industrial efforts to improve battery lifetime.Comment: Submitted to the Journal of the Electrochemical Societ

Piecewise-linear modelling with automated feature selection for Li-ion battery end-of-life prognosis

The complex nature of lithium-ion battery degradation has led to many machine learning-based approaches for health forecasting being proposed in the literature. However, machine learning using sophisticated models can be computationally expensive, and although linear models are faster they can also be inflexible. Piecewise-linear models offer a compromise—a fast and flexible alternative that is not as computationally expensive as techniques such as neural networks or Gaussian process regression. Here, a piecewise-linear approach for battery health forecasting, including an automated feature selection step, is compared to a Gaussian process regression model and found to perform equally well in terms of the median error on a training dataset, and indeed somewhat better at the 95th percentile of error. The feature selection process demonstrates the benefit of limiting the correlation between inputs. Further trials found that the piecewise-linear approach was robust to changing input size and availability of training data

Current-driven solvent segregation in lithium-ion electrolytes

Liquid lithium-battery electrolytes universally incorporate at least two solvents to balance conductivity and viscosity. Almost all continuum models treat cosolvent systems such as ethylene carbonate:ethyl-methyl carbonate (EC:EMC) as single entities whose constituents travel with identical velocities. We test this “single-solvent approximation” by subjecting LiPF6:EC:EMC blends to constant-current polarization in Hittorf experiments. A Gaussian process regression model trained on physicochemical properties quantifies changes in composition across the Hittorf cell. EC and EMC are found to migrate at noticeably different rates under applied current, demonstrating conclusively that the single-solvent approximation is violated and that polarization of salt concentration is anticorrelated with that of EC. Simulations show extreme solvent segregation near electrode/liquid interfaces: a 5% change in EC:EMC ratio, post-Hittorf polarization, implies more than a 50% change adjacent to the interface during the current pulse. Understanding how lithium-ion flux induces local cosolvent or additive imbalances suggests new approaches to electrolyte design

Estimation of Li‐Ion Degradation Test Sample Sizes Required to Understand Cell‐to‐Cell Variability**

Ageing of lithium-ion batteries results in irreversible reduction in performance. Intrinsic variability between cells, caused by manufacturing differences, occurs throughout life and increases with age. Researchers need to know the minimum number of cells they should test to give an accurate representation of population variability, since testing many cells is expensive. In this paper, empirical capacity versus time ageing models were fitted to various degradation datasets for commercially available cells assuming the model parameters could be drawn from a larger population distribution. Using a hierarchical Bayesian approach, we estimated the number of cells required to be tested. Depending on the complexity, ageing models with 1, 2 or 3 parameters respectively required data from at least 9, 11 or 13 cells for a consistent fit. This implies researchers will need to test at least these numbers of cells at each test point in their experiment to capture manufacturing variability

A minimal information set to enable verifiable theoretical battery research

Batteries are an enabling technology for addressing sustainability through the electrification of various forms of transportation (1) and grid storage. (2) Batteries are truly multi-scale, multi-physics devices, and accordingly various theoretical descriptions exist to understand their behavior (3−5) ranging from atomistic details to techno-economic trends. As we explore advanced battery chemistries (6,7) or previously inaccessible aspects of existing ones, (8−10) new theories are required to drive decisions. (11−13) The decisions are influenced by the limitations of the underlying theory. Advanced theories used to understand battery phenomena are complicated and require substantial effort to reproduce. However, such constraints should not limit the insights from these theories. We can strive to make the theoretical research verifiable such that any battery stakeholder can assess the veracity of new theories, sophisticated simulations or elaborate analyses. We distinguish verifiability, which amounts to “Can I trust the results, conclusions and insights and identify the context where they are relevant?”, from reproducibility, which ensures “Would I get the same results if I followed the same steps?” With this motivation, we propose a checklist to guide future reports of theoretical battery research in Table 1. We hereafter discuss our thoughts leading to this and how it helps to consistently document necessary details while allowing complete freedom for creativity of individual researchers. Given the differences between experimental and theoretical studies, the proposed checklist differs from its experimental counterparts. (14,15) This checklist covers all flavors of theoretical battery research, ranging from atomic/molecular calculations (16−19) to mesoscale (20,21) and continuum-scale interactions, (9,22) and techno-economic analysis. (23,24) Also, as more and more experimental studies analyze raw data, (25) we feel this checklist would be broadly relevant