Eyring acceleration model for predicting calendar ageing of lithium-ion batteries

Abstract

Modelling of lithium-ion batteries calendar ageing is often based on a semi-empirical approach by using, for example the Arrhenius acceleration model. Our approach is based on Eyring acceleration model, which is not widely used for electrochemical energy storage components. Parameter identification is typically performed without taking into account the state-of-charge (SoC) drifting. However, even in rest condition, battery cells' SoC drifts because of capacity losses (self-discharge and capacity fade). In this work we have taken into account the SoC drift during calendar ageing tests. For this, we considered available capacity (Ah) instead of SoC (%) as ageing factor. Then, the analytical solution of the problem leads to the use of the Lambert W function in the model formulation. Simulation results show that Lambert-Eyring model is more accurate and allows a reduction in the number of parameters to be identified.

Multi-Objective Optimisation of the Management of Electric Bus Fleet Charging

“…2) contains a previously developed electric model based on VEHLIB, a simple thermal model of a large prismatic Li-ion battery (LiFePO4/graphite) based on an equivalent electrical circuit [8] and an Eyring acceleration model for predicting calendar ageing of lithium-ion batteries [9].…”

Gaussian Process Regression With Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries

“…In order to demonstrate the effectiveness of the proposed GPR+ARD model, a simplified regression calendar-life (RCL) model is also adopted and compared. This RCL model is actually a typical semi-empirical model which has been applied in publication [11]. Generally, the capacity loss ∆Q during storage is expressed as a function of the battery SOC SOC sto , storage temperature T sto , and storage time duration t as [11]:…”

“…Then (13) can be simplified by assuming decoupling. First, both battery SOC and T s versus time, implying that the calendar degradation trend is similar over time and can be shaped by a coefficient as [11]:…”

“…In the literature, the shaping coefficient C(SOC sto , T sto ) is usually assumed to follow the Arrenhius relation, allowing the SOC sto and T sto to be decoupled as [11]:…”

“…Petit et al [10] developed an empirical capacity loss model to evaluate the effects of SOC and temperature on storage lifetime of Li-ion batteries. In [11], instead of using Arrhenius acceleration model, a semi-empirial approach based on the Eyring acceleration model was adopted to predict battery calendar aging, while the SOC drifting was also taken into account. By considering the effects of temperature and storage conditions, De Hoog et al [12] proposed a semiempirical combined model to estimate the calendar lifetime for a Nickel-manganese-Cobalt Oxide battery.…”