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.

In this paper, the first physics-based model including direct links between four degradation mechanisms in the negative electrode is reported.

Physics-based electrochemical battery models derived from porous electrode theory are a very powerful tool for understanding lithium-ion batteries, as well as for improving their design and management. Different model fidelity, and thus model complexity, is needed for different applications. For example, in battery design we can afford longer computational times and the use of powerful computers, while for real-time battery control (e.g. in electric vehicles) we need to perform very fast calculations using simple devices. For this reason, simplified models that retain most of the features at a lower computational cost are widely used. Even though in the literature we often find these simplified models posed independently, leading to inconsistencies between models, they can actually be derived from more complicated models using a unified and systematic framework. In this review, we showcase this reductive framework, starting from a high-fidelity microscale model and reducing it all the way down to the single particle model, deriving in the process other common models, such as the Doyle–Fuller–Newman model. We also provide a critical discussion on the advantages and shortcomings of each of the models, which can aid model selection for a particular application. Finally, we provide an overview of possible extensions to the models, with a special focus on thermal models. Any of these extensions could be incorporated into the microscale model and the reductive framework re-applied to lead to a new generation of simplified, multi-physics models.

Differential voltage analysis (DVA) is a conventional approach for estimating capacity degradation in batteries. During charging, a graphite electrode goes through several phase transitions observed as plateaus in the voltage response. The transitions between these plateaus emerge as observable peaks in the differential voltage. The DVA method utilizes these peaks for estimating cell degradation. Unfortunately, at higher C-rates (above C/2) the peaks flatten and become unobservable. In this work, we show that, unlike the differential voltage, the peaks in the 2nd derivative of the expansion with respect to capacity remain observable up to 1C and thus make possible diagnostic algorithms at these charging rates. To understand why that is the case, we have developed an electrochemical and expansion model suitable for model-based estimation. In particular, we demonstrate that the single particle modeling methodology is not able to capture the peak smoothing effect, therefore a multi-particle approach for the graphite electrode is needed. Additionally, model parameters are identified using experimental data from a graphite/NMC pouch cell. The proposed model produces an excellent fit for the observed electric and mechanical swelling response of the cells and could enable physics-based data-driven degradation studies at practical charging rates.

We propose algorithms to speed up physics-based battery lifetime simulations by one to two orders of magnitude compared to the state-of-the-art. First, we propose a reformulation of the Single Particle Model with side reactions to remove algebraic equations and hence reduce stiffness, with 3x speed-up in simulation time (intra-cycle reformulation). Second, we introduce an algorithm that makes use of the difference between the “fast” timescale of battery cycling and the “slow” timescale of battery degradation by adaptively selecting and simulating representative cycles, skipping other cycles, and hence requires fewer cycle simulations to simulate the entire lifetime (adaptive inter-cycle extrapolation). This algorithm is demonstrated with a specific degradation mechanism but can be applied to various models of aging phenomena. In the particular case study considered, simulations of the entire lifetime are performed in under 5 s. This opens the possibility for much faster and more accurate model development, testing, and comparison with experimental data.