Lithium-ion batteries are typically modeled using porous electrode theory coupled with various transport and reaction mechanisms, along with suitable discretization or approximations for the solid-phase diffusion equation. The solid-phase diffusion equation represents the main computational burden for typical pseudo-2-dimensional (p2D) models since these equations in the pseudo r-dimension must be solved at each point in the computational grid. This substantially increases the complexity of the model as well as the computational time. Traditional approaches towards simplifying solid-phase diffusion possess certain significant limitations, especially in modeling emerging electrode materials which involve phase changes and variable diffusivities. A computationally efficient representation for solid-phase diffusion is discussed in this paper based on symmetric polynomials using Orthogonal Collocation and Galerkin formulation (weak form). A systematic approach is provided to increase the accuracy of the approximation (p form in finite element methods) to enable efficient simulation with a minimal number of semi-discretized equations, ensuring mass conservation even for non-linear diffusion problems involving variable diffusivities. These methods are then demonstrated by incorporation into the full p2D model, illustrating their advantages in simulating high C-rates and short-time dynamic operation of Lithium-ion batteries.
Capacity degradation of batteries negatively impacts the lifetime of battery packs as well as the residual value of electric vehicles. Developing a degradation model for the prognosis of the state of health (SOH) under storage conditions is a critical aspect of developing algorithms to maximize the remaining useful lifetime of these systems. It is known that electrochemical degradation models have superior predictive ability compared to more empirical or data-driven models, but these still require improvement in terms of computational efficiency. In this work, we thus introduce a simple, reduced-order electrochemical degradation model for lithium-ion batteries. This model considers three key aging mechanisms with the ability to predict the SOH under various calendar aging conditions. Lumped model results are validated against a single particle-based degradation model and show close agreement, even as the simulation time is reduced by 2 orders of magnitude. This indicates significant potential in real-world applications to account and correct for the effects of storage on cell performance and lifetime.
Capacity fade experienced by the battery will affect the lifetime of the battery pack as well as the residual value of an electric vehicle. Developing a degradation model that can prognosis state of health for the given operating condition is critical for developing an algorithm to maximize the remaining useful lifetime of the systems. It is known that the electrochemical degradation model has superior predictability to other data-driven models, but still needs improvement in terms of computational efficiency. We reduced the electrochemical degradation model which considers three main aging mechanisms that can predict the state of health at various calendric aging conditions. The parameterization procedure and its prediction capability will be also discussed.
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