Neural Network-Based Li-Ion Battery Aging Model at Accelerated C-Rate

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 reliability become critical as they lose capacity with increasing charge discharging cycles. Moreover, subject to aging EVs due load variations discharge. Monitoring the battery cycle life at various discharge rates would enable management system (BMS) implement control parameters resolve issue. In this paper, a lifetime degradation model is proposed an accelerated current rate (C-rate). Furthermore, ideal within standard C-rate beyond proposed. The consequence on thoroughly investigated. investigated deep learning algorithm-based feed-forward neural network (FNN), recurrent (RNN) long short-term memory (LSTM) layer. A comparative assessment developed models carried out it shown that LSTM-RNN has compared traditional FNN network.