Neural network-based Li-Ion battery aging model at accelerated C-Rate - Hoque, Md Azizul; Hassan, Mohd Khair; Hajjo, Abdulrahman; Tokhi, Mohammad Osman (Multidisciplinary Digital Publishing Institute, 2023)
Lithium-ion (Li-ion) batteries are widely used in electric vehicles (EVs) because of their high energy density, low self-discharge, and superior performance. Despite this, Li-ion batteries’ performance and reliability become critical as they lose their capacity with increasing charge and discharging cycles. Moreover, Li-ion batteries are subject to aging in EVs due to load variations in discharge. Monitoring the battery cycle life at various discharge rates would enable the battery management system (BMS) to implement control parameters to resolve the aging issue. In this paper, a battery lifetime degradation model is proposed at an accelerated current rate (C-rate). Furthermore, an ideal lifetime discharge rate within the standard C-rate and beyond the C-rate is proposed. The consequence of discharging at an accelerated C-rate on the cycle life of the batteries is thoroughly investigated. Moreover, the battery degradation model is investigated with a deep learning algorithm-based feed-forward neural network (FNN), and a recurrent neural network (RNN) with long short-term memory (LSTM) layer. A comparative assessment of performance of the developed models is carried out and it is shown that the LSTM-RNN battery aging model has superior performance at accelerated C-rate compared to the traditional FNN network.