Modeling and Experimental Determination of Lithium-Ion Battery Degradation in Hot Environment

Abstract

Lithium-ion batteries (LIB) became the most important energy storage systems (ESS) for different applications such as renewable energy systems and electric vehicles due to their outstanding performance such as the high charging/discharging efficiencies, low discharge rate, high power, and energy densities, long lifetime, and continued cost reduction. An accurate degradation model for LIBs is a crucial issue to improve their performance in different operating conditions. The effect of temperature, state of charge, and other factors have been considered in modeling the LIB. Two different modeling strategies have been discussed, the first one is based on the theoretical lifetime equation, meanwhile, the other one is based on empirical lifetime equations. The current proposed model provides a novel approach for estimating the degradation of LIB batteries based on empirical lifespan equations. Many experimental efforts with an accelerated profile of the state of health under different operating conditions have been conducted for two different models of LIB Iron Phosphate (LFP) with 2.4 kWh energy and 50Ah capacity. Parameters estimation of the modeling has been determined using the modified particle swarm optimization (MPSO) algorithm. In this algorithm, the number of particles will be reduced gradually with iterations to enhance the global search and reduce the convergence time compared to the original PSO algorithm. Cost estimations of storage have been deduced for all batteries under study and for different operating conditions. For comparison, two different LIBs and one valve-regulated lead acid battery are used in this study. The LIB batteries are showing a substantial reduction in cost compared to lead acid batteries. One of the two LIBs is not substantially affected by the temperature meanwhile the other one is showing substantial deterioration in performance with temperature increase.