An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries

2022 ◽  
Vol 156 ◽  
pp. 111843
Author(s):  
Penghua Li ◽  
Zijian Zhang ◽  
Radu Grosu ◽  
Zhongwei Deng ◽  
Jie Hou ◽  
...  
Keyword(s):  
Neural Network ◽  
Electric Vehicle ◽  
Lithium Batteries ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
State Of Health ◽  
Useful Life ◽  
End To End

Short-term prediction of remaining life for lithium-ion battery based on adaptive hybrid model with Long Short-Term Memory neural network and optimized particle filter

2021 ◽  
pp. 1-36
Author(s):  
Ning He ◽  
Cheng Qian ◽  
Lile He
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Life Prediction ◽  
Short Term Memory ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Short Term ◽  
Term Memory ◽  
Useful Life ◽  
Long Short Term Memory

Abstract As an important energy storage device, lithium-ion batteries have vast applications in daily production and life. Therefore, the remaining useful life prediction of such batteries is of great significance, which can maintain the efficacy and reliability of the system powered by lithium-ion batteries. For predicting remaining useful life of lithium-ion batteries accurately, an adaptive hybrid battery model and an improved particle filter are developed. Firstly, the adaptive hybrid model is constructed, which is a combination of empirical model and long-short term memory neural network model such that it could characterize battery capacity degradation trend more effectively. In addition, the adaptive adjustment of the parameters for hybrid model is realized via optimization technique. Then, the beetle antennae search based particle filter is applied to update the battery states offline constructed by the proposed adaptive hybrid model, which can improve the estimation accuracy. Finally, remaining useful life short-term prediction is realized online based on long short-term memory neural network rolling prediction combined historical capacity with online measurements and latest offline states and model parameters. The battery data set published by NASA is used to verify the effectiveness of proposed strategy. The experimental results indicate that the proposed adaptive hybrid model can well represent the battery degradation characteristics, and have a higher accuracy compared with other models. The short-term remaining useful life prediction results have good performance with the errors of 1 cycle, 3 cycles, and 1 cycle, above results indicate proposed scheme has a good performance on short-term remaining useful life prediction.

scholarly journals Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881718 ◽  
Author(s):  
Wentao Mao ◽  
Jianliang He ◽  
Jiamei Tang ◽  
Yuan Li
Keyword(s):  
Neural Network ◽  
Life Prediction ◽  
Short Term Memory ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Feature Representation ◽  
Short Term ◽  
Term Memory ◽  
Useful Life ◽  
Long Short Term Memory

For bearing remaining useful life prediction problem, the traditional machine-learning-based methods are generally short of feature representation ability and incapable of adaptive feature extraction. Although deep-learning-based remaining useful life prediction methods proposed in recent years can effectively extract discriminative features for bearing fault, these methods tend to less consider temporal information of fault degradation process. To solve this problem, a new remaining useful life prediction approach based on deep feature representation and long short-term memory neural network is proposed in this article. First, a new criterion, named support vector data normalized correlation coefficient, is proposed to automatically divide the whole bearing life as normal state and fast degradation state. Second, deep features of bearing fault with good representation ability can be obtained from convolutional neural network by means of the marginal spectrum in Hilbert–Huang transform of raw vibration signals and health state label. Finally, by considering the temporal information of degradation process, these features are fed into a long short-term memory neural network to construct a remaining useful life prediction model. Experiments are conducted on bearing data sets of IEEE PHM Challenge 2012. The results show the significance of performance improvement of the proposed method in terms of predictive accuracy and numerical stability.

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.

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