Abstract:
For electric vehicles, lithium-ion (Li-ion) batteries serve as the primary energy source.
Accurately estimating and predicting the state of health (SOH) and remaining useful life
(RUL) of lithium-ion batteries play an increasingly crucial role in intelligent battery health
management systems. Additionally, it serves as a battery failure early warning system.
Model-based and data-driven approaches can be broadly classified as the two types of ap proaches that have been documented in recent years for SOH estimation and RUL assessment
of lithium-ion batteries. Among these, data-driven techniques can produce more accurate
predictions. This work focuses on developing a deep learning model for battery parameter
estimation in electric vehicles. A Temporal Convolutional Network (TCN) model is proposed
for SOH monitoring and RUL assessment in Lithium-ion batteries before the failure of the
battery. Three analytical indices: RMSE, MAE and R SQUARE are chosen to evaluate the
prediction results numerically. The proposed model is experimented and tested using NASA
lithium-ion battery health dataset. The prediction results of both SOH and RUL showed
good accuracy of 99% indicating that the proposed model has high robustness and good
performance.
