A Review of Lithium-Ion Battery Empirical and Semi-Empirical Aging Models for Off-Grid Renewable Energy Systems Application

Abstract

Aging of lithium-ion (Li-ion) batteries in off-grid renewable energy systems (RESs) can be monitored and controlled using battery management systems (BMSs) which utilize battery aging models. Empirical and semi-empirical models (EMs) of battery aging are preferred for BMSs due to their simplicity and intuitiveness. This study is unique as it aims at identifying appropriate empirical and semi-EMs, in terms of complexity and current fluctuation representation, for BMS for off-grid RESs. Different EMs of Li-ion battery calendar and cycle aging have been extracted from literature and compared mainly in terms of complexity, current fluctuation representation, and modeling of capacity fade and resistance increase. The extracted models have been put in groups which are based on modeling format used, namely: calendar aging (only) models (CAOM), cycle aging (only) models (CYAOM), calendar and cycle aging (separated) models (CCYAOM), and calendar and cycle aging (combined) models (CCYACM). Results show that three models meet requirements for BMS for off-grid RESs. The three models fall under CYAOM, CCYAOM, and CCYACM. The study further finds that 54% of EMs model current fluctuation as an aging factor, 92% model aging in terms of capacity fade, and 46% model aging as resistance increase. Furthermore, the study recommends comparison of EMs through simulations to further validate the different listed models. It also recommends evaluation of the models to establish an appropriate way of representing Li-ion battery aging, whether in terms of capacity fade or resistance increase.