Particle Learning Framework for Estimating the Remaining Useful Life of Lithium-Ion Batteries

2017 ◽  
Vol 66 (2) ◽  
pp. 280-293 ◽  
Author(s):  
Zhenbao Liu ◽  
Gaoyuan Sun ◽  
Shuhui Bu ◽  
Junwei Han ◽  
Xiaojun Tang ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Learning Framework ◽  
Useful Life ◽  
Particle Learning

Remaining Useful Life Prediction of Lithium-ion Batteries Using Multiple Kernel Extreme Learning Machine

2020 ◽  
Vol 13 ◽  
Author(s):  
Renxiong Liu
Keyword(s):  
Lithium Ion Batteries ◽  
Extreme Learning Machine ◽  
Kernel Function ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Prediction Methods ◽  
Multiple Kernel ◽  
Kernel Extreme Learning Machine ◽  
Useful Life ◽  
Learning Machine

Objective: Lithium-ion batteries are important components used in electric automobiles (EVs), fuel cell EVs and other hybrid EVs. Therefore, it is greatly important to discover its remaining useful life (RUL). Methods: In this paper, a battery RUL prediction approach using multiple kernel extreme learning machine (MKELM) is presented. The MKELM’s kernel keeps diversified by consisting multiple kernel functions including Gaussian kernel function, Polynomial kernel function and Sigmoid kernel function, and every kernel function’s weight and parameter are optimized through differential evolution (DE) algorithm. Results : Battery capacity data measured from NASA Ames Prognostics Center are used to demonstrate the prediction procedure of the proposed approach, and the MKELM is compared with other commonly used prediction methods in terms of absolute error, relative accuracy and mean square error. Conclusion: The prediction results prove that the MKELM approach can accurately predict the battery RUL. Furthermore, a compare experiment is executed to validate that the MKELM method is better than other prediction methods in terms of prediction accuracy.

scholarly journals A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries

2021 ◽  
Vol 7 ◽  
pp. 5562-5574 ◽  
Author(s):  
Shunli Wang ◽  
Siyu Jin ◽  
Dekui Bai ◽  
Yongcun Fan ◽  
Haotian Shi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lithium Ion Batteries ◽  
Critical Review ◽  
Life Prediction ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Learning Methods ◽  
Useful Life

scholarly journals Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7521
Author(s):  
Shaheer Ansari ◽  
Afida Ayob ◽  
Molla Shahadat Hossain Lipu ◽  
Aini Hussain ◽  
Mohamad Hanif Md Saad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Lithium Ion Batteries ◽  
Electric Vehicle ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Systematic Sampling ◽  
Channel Input ◽  
Useful Life ◽  
Artificial Neural

Remaining useful life (RUL) is a crucial assessment indicator to evaluate battery efficiency, robustness, and accuracy by determining battery failure occurrence in electric vehicle (EV) applications. RUL prediction is necessary for timely maintenance and replacement of the battery in EVs. This paper proposes an artificial neural network (ANN) technique to predict the RUL of lithium-ion batteries under various training datasets. A multi-channel input (MCI) profile is implemented and compared with single-channel input (SCI) or single input (SI) with diverse datasets. A NASA battery dataset is utilized and systematic sampling is implemented to extract 10 sample values of voltage, current, and temperature at equal intervals from each charging cycle to reconstitute the input training profile. The experimental results demonstrate that MCI profile-based RUL prediction is highly accurate compared to SCI profile under diverse datasets. It is reported that RMSE for the proposed MCI profile-based ANN technique is 0.0819 compared to 0.5130 with SCI profile for the B0005 battery dataset. Moreover, RMSE is higher when the proposed model is trained with two datasets and one dataset, respectively. Additionally, the importance of capacity regeneration phenomena in batteries B0006 and B0018 to predict battery RUL is investigated. The results demonstrate that RMSE for the testing battery dataset B0005 is 3.7092, 3.9373 when trained with B0006, B0018, respectively, while it is 3.3678 when trained with B0007 due to the effect of capacity regeneration in B0006 and B0018 battery datasets.

scholarly journals Data-Driven Remaining Useful Life Prediction for Lithium-Ion Batteries Using Multi-Charging Profile Framework: A Recurrent Neural Network Approach

2021 ◽  
Vol 13 (23) ◽  
pp. 13333
Author(s):  
Shaheer Ansari ◽  
Afida Ayob ◽  
Molla Shahadat Hossain Lipu ◽  
Aini Hussain ◽  
Mohamad Hanif Md Saad
Keyword(s):  
Neural Network ◽  
Lithium Ion Batteries ◽  
Recurrent Neural Network ◽  
Single Channel ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Channel Input ◽  
Capacity Degradation ◽  
Battery Capacity ◽  
Useful Life

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.

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