Characteristics of Li-Ion Battery at Accelerated C-Rate with Deep Learning Method

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%.

Automated summary

This research tested lithium-ion batteries at different charge and discharge rates and proposed a capacity fade model to interpret their vulnerabilities in energy storage systems. The study found that charging and discharging the batteries at accelerated rates accelerates their aging. Furthermore, the capacity fade model was investigated using deep learning algorithms and it was discovered that the LSTM-RNN battery aging model performed better than the conventional FNN network.