Abstract
Remaining Useful Life (RUL) prediction for lithium-ion batteries has received increasing attention as it evaluates the reliability of batteries to determine the advent of failure and mitigate battery risks. The accurate prediction of RUL can ensure safe operation and prevent risk failure and unwanted catastrophic occurrence of the battery storage system. However, precise prediction for RUL is challenging due to the battery capacity degradation and performance variation under temperature and aging impacts. Therefore, this paper proposes the Multi-Channel Input (MCI) profile with the Recurrent Neural Network (RNN) algorithm to predict RUL for lithium-ion batteries under the various combinations of datasets. Two methodologies, namely the Single-Channel Input (SCI) profile and the MCI profile, are implemented, and their results are analyzed. The verification of the proposed model is carried out by combining various datasets provided by NASA. The experimental results suggest that the MCI profile-based method demonstrates better prediction results than the SCI profile-based method with a significant reduction in prediction error with regard to various evaluation metrics. Additionally, the comparative analysis has illustrated that the proposed RNN method significantly outperforms the Feed Forward Neural Network (FFNN), Back Propagation Neural Network (BPNN), Function Fitting Neural Network (FNN), and Cascade Forward Neural Network (CFNN) under different battery datasets.
Highlights
The battery storage system has received significant consideration in addressing carbon emissions and climate change problems [1,2]
Four data-driven models are employed for the comparative analysis, which are Back Propagation Neural Network (BPNN), Fitting Neural Network (FNN), FFN, and Cascade Forward Neural Network (CFNN), respectively
The training of each model is performed with 70:30 data, it is concluded that the capability of BPNN, FNN, Feed Forward Neural Network (FFNN), and CFNN is not comprehensive with regard to regeneration phenomena compared to Recurrent Neural Network (RNN) due to an insufficient feedback connection structure, resulting in its low ‘memory’ ability
Summary
Shaheer Ansari 1 , Afida Ayob 1,2, * , Molla Shahadat Hossain Lipu 1,2, * , Aini Hussain 1 and Mohamad Hanif Md Saad 3,4.
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