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In this research, Lithium-ion (Li-ion) batteries were tested at four different charge rates (DCR): 0.2C, 0.5C, 1C, and 1.5C, and four different discharge rates (DDR): 0.5C, 0.9C, 1.3C, 1.6C. This paper proposes a capacity fade model for charging and discharging at accelerated current-rate (C-rate), to interpret the vulnerabilities of Li-ion batteries in energy storage system, because Lithium-ion (Li-ion) batteries are prone to ageing at the fluctuation of the loads in micro-grids. The characteristics of Li-ion batteries both at accelerated DCR and DDR are thoroughly investigated. It is discovered that charging and discharging Li-ion batteries outside of the standard C-rate accelerates their ageing. In addition, the degree of capacity fade is assessed at an accelerated C-rate to develop an ideal charge and discharge model for the micro-grids. Furthermore, the battery capacity fade model is then investigated with deep learning algorithm-based feed-forward neural network (FNN), and recurrent neural network with long-short term memory layer (RNN-LSTM). A comparison of the developed capacity fade models is performed, and it is discovered that the LSTM-RNN battery ageing model outperforms the conventional FNN network at accelerated C-rate. Nevertheless, the error metrics performance of both FNN and LSTM-RNN are less than 0.1%. [ABSTRACT FROM AUTHOR]