A Phenomenological Degradation Model for Cyclic Aging of Lithium Ion Cell Materials

Abstract

A phenomenological Li-ion cell degradation model, pertaining to charge-discharge cyclic fatigue, is proposed and validated. It is known that Solid Electrolyte Inter-phase (SEI) formation on the particle surfaces consumes active Li leading to capacity loss. The problem is further aggravated by the creation of fresh surfaces by the fracture that develops as a result of intercalation induced stresses. In addition, fracture could result in isolation of chunks of electrode material or SEI could electronically isolate certain electrode material zones, both essentially rendering the active Li or electrode material ineffective. The degradation leads to increase in electronic resistance and decrease of ionic conductivity as well as diffusivity. Central to the model is a parameter expressed as the normalized reaction surface area, diminishing with charge-discharge cycles. Here, we develop phenomenological evolution expressions for the Fracture, SEI formation and Isolation, and incorporate them in Newman's Porous Composite Electrode framework. The model is implemented in the battery module of COMSOL. Notably, the utility of a lumped parameter ‘ΔSOC*SOCmean’, based on the State of Charge (SOC) is brought out.

Characterising Lithium-Ion Battery Degradation through the Identification and Tracking of Electrochemical Battery Model Parameters

“…The diffevol algorithm is the Maple implementation of DE innovated by Storn and Price [17,18]. While traditional optimization methods such as the quasi-newton method requires the optimization problem to be differentiable, DE does not use the gradient function of the problem being optimized and therefore is more suitable for optimization problems that are variable, noisy and non-continuous.…”

“…An important contribution to parameter identification was made by Forman et al [15] who identified the full set of parameters (88 scalars and function control points) of the P2D model using a genetic algorithm derived from the drive-cycle data from a plug-in hybrid electric vehicle (PHEV). In the context of battery degradation, perturbative terms have been introduced into the SP model and then identification used to obtain values of these perturbative parameters [16,17]. In the case of well-understood ageing mechanisms such as film formation on the negative electrode where models of degradation already exist, as was considered in [16,17], this approach is suitable.…”

“…In the context of battery degradation, perturbative terms have been introduced into the SP model and then identification used to obtain values of these perturbative parameters [16,17]. In the case of well-understood ageing mechanisms such as film formation on the negative electrode where models of degradation already exist, as was considered in [16,17], this approach is suitable. However, for mechanisms that are not well understood, it is difficult to introduce perturbative terms into the model because the appropriate governing equations for these perturbative terms are non-existent or not well established.…”

Reduced Order Modeling of Mechanical Degradation Induced Performance Decay in Lithium-Ion Battery Porous Electrodes

“…13 Coupling of mechanical and chemical degradation using ROMs has been conducted to investigate the enhancement in capacity fade due to SEI growth on microcracks located on active particle surfaces.…”

“…12 Crack propagation, rupture, and isolation of portions of active particles can also cause loss of active sites where lithium atoms can intercalate, resulting in effective capacity fade. 13 In the past two to three decades, capacity fade due to the formation of SEI 8,10,[14][15][16][17] and lithium plating 18,19 have been investigated thoroughly. On the other hand, resistance growth and capacity fade due to delamination and site loss have not been explored extensively.…”