Simplified Mechanistic Aging Model for Lithium Ion Batteries in Large-Scale Applications.

Energy storage systems play a vital role in balancing solar- and wind-generated power. However, the uncertainty of their lifespan is a key factor limiting their large-scale applications. While currently reported battery aging models, empirical or semi-empirical, are capable of accurately assessing battery decay under specific operating conditions, they cannot reliably predict the battery lifespan beyond the measured data. Moreover, these models generally require a tedious procedure to determine model parameters, reducing their value for onsite applications. This paper, based on Newman's pseudo-2D performance model and incorporating microparameters obtained from cell disassembly, developed a mechanistic model accounting for three major aging mechanisms of lithium iron phosphate/graphite cells, i.e., solid electrolyte interphase growth, lithium plating, and gas generation. The prediction of this mechanistic model agrees with the experimental results within an average error of ±1%. The mechanistic model was further simplified into an engineering model consisting of only two core parameters, loss of active lithium and loss of active material, and was more suitable for large-scale applications. The accuracy of the engineering model was validated in a 100 MW/200 MWh energy storage project. When the actual State of Health (SOH) of the battery degraded to 89.78%, the simplified model exhibited an error of -0.17%, and the computation time decreased from 8.12 h to 10 s compared to the mechanistic model.