Executive Summary : | For fast and cost-effective cycle life estimation of LiBs, this project employs the accelerated testing method where the batteries are subject to higher levels of stress factors (ambient temperature, charge-discharge rate, depth of discharge, etc.) as compared to the normal test or operation conditions. In such harsh conditions, the battery aging is accelerated, and the battery can reach its EOL in a few days. Now that we have a reference of cycle life in harsh conditions, it can be translated into cycle life in normal conditions by defining an acceleration factor (ratio of cycle lives of battery during normal and accelerated aging conditions). However, this acceleration factor is unknown for a new battery. This project aims to design accelerated aging protocols for which the acceleration factor is valid across batteries from different manufacturers. This overall task is challenging and shall take a longer time if executed with mere experiments. We first conduct experimental investigations to identify the stress factors that indeed result in accelerated aging. Our studies shall be carried out with cylindrical LFP (lithium iron phosphate) cells, which are affordable and safer in Indian perspective. The collected experimental data shall then be used to develop a physics-based mathematical model that can capture the accelerated aging behavior of the battery. The model shall be used to conduct wide-ranging parametric studies with respect to the various stress factors. Combining experimental and modeling data, we aim to unveil the correlation between the aging acceleration factor and various stress factors. Model simulations shall be carried out to simulate the batteries from different manufacturers by varying electrode design parameters and material properties. This systematic methodology shall allow us to identify accelerated aging protocol for which the acceleration factor is widely applicable. Overall, the project would benefit manufacturers of EVs, energy storage system (Ess), and other LiB-powered applications by providing them with a cost-effective way to estimate the life cycle of batteries in various scenarios. |