scholarly journals Dynamic Long Short-Term Memory Neural-Network- Based Indirect Remaining-Useful-Life Prognosis for Satellite Lithium-Ion Battery

2018 ◽  
Vol 8 (11) ◽  
pp. 2078 ◽  
Author(s):  
Cunsong Wang ◽  
Ningyun Lu ◽  
Senlin Wang ◽  
Yuehua Cheng ◽  
Bin Jiang
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Li Ion Batteries ◽  
Short Term ◽  
Term Memory ◽  
Li Ion ◽  
Useful Life ◽  
Long Short Term Memory

On-line remaining-useful-life (RUL) prognosis is still a problem for satellite Lithium-ion (Li-ion) batteries. Meanwhile, capacity, widely used as a health indicator of a battery (HI), is inconvenient or even impossible to measure. Aiming at practical and precise prediction of the RUL of satellite Li-ion batteries, a dynamic long short-term memory (DLSTM) neural-network-based indirect RUL prognosis is proposed in this paper. Firstly, an indirect HI based on the Spearman correlation analysis method is extracted from the battery discharge voltages, and the relationship between the indirect HI indices and battery capacity is established using a polynomial fitting method. Then, by integrating the Adam method, L2 regularization method, and incremental learning, a DLSTM method is proposed and applied for Li-ion battery RUL prognosis. Finally, verification of the results on NASA #5 battery data sets demonstrates that the proposed method has better dynamic performance and higher accuracy than the three other popular methods.

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 Adaptive Online State of Charge Estimation of EVs Lithium-Ion Batteries with Deep Recurrent Neural Networks

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 758
Author(s):  
Gelareh Javid ◽  
Djaffar Ould Abdeslam ◽  
Michel Basset
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Lithium Ion ◽  
State Of Charge ◽  
Li Ion Batteries ◽  
Short Term ◽  
Term Memory ◽  
Soc Estimation ◽  
Li Ion ◽  
Long Short Term Memory

The State of Charge (SOC) estimation is a significant issue for safe performance and the lifespan of Lithium-ion (Li-ion) batteries. In this paper, a Robust Adaptive Online Long Short-Term Memory (RoLSTM) method is proposed to extract SOC estimation for Li-ion Batteries in Electric Vehicles (EVs). This real-time, as its name suggests, method is based on a Recurrent Neural Network (RNN) containing Long Short-Term Memory (LSTM) units and using the Robust and Adaptive online gradient learning method (RoAdam) for optimization. In the proposed architecture, one sequential model is defined for each of the three inputs: voltage, current, and temperature of the battery. Therefore, the three networks work in parallel. With this approach, the number of LSTM units are reduced. Using this suggested method, one is not dependent on precise battery models and can avoid complicated mathematical methods. In addition, unlike the traditional recursive neural network where content is re-written at any time, the LSTM network can decide on preserving the current memory through the proposed gateways. In that case, it can easily transfer this information over long paths to receive and maintain long-term dependencies. Using real databases, the experiment results illustrate the better performance of RoLSTM applied to SOC estimation of Li-Ion batteries in comparison with a neural network modeling and unscented Kalman filter method that have been used thus far.

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.

Remaining Useful Life Predictions for Turbofan Engine Degradation Using Online Long Short-Term Memory Network

2019 ◽  
Author(s):  
Pallabi Kakati ◽  
Devendra Dandotiya ◽  
Bhaskar Pal
Keyword(s):  
Neural Network ◽  
Time Domain ◽  
Short Term Memory ◽  
Markov Models ◽  
Health Management ◽  
Remaining Useful Life ◽  
Short Term ◽  
Term Memory ◽  
Useful Life ◽  
Long Short Term Memory

Abstract In any industrial system, accurate prediction of Remaining Useful Life (RUL) is important for Prognostics and Health Management (PHM), so as to detect breakdown of system well in advance and take proper measures. Different methods are available in the literature that have been proposed for prediction of RUL. Among these methods, the data driven method is accepted to be the most reliable by many researchers, due to the use of real time sensor based vibrational and/or pressure data. These data are acquired in time domain. Methods such as Recurrent Neural Networks (RNNs), Convolutional Neural Network (CNN), Hidden Markov Models (HMMs) are generally applied in this area. Nevertheless, all these methods have issues while dealing with dependencies in these data. In this context, Long Short-Term Memory (LSTM) neural network has been proposed to deal with these dependencies while predicting RUL of any system. The LSTM model has the advantage of retaining time domain information for a long duration of time. However, with the arrival of new data, the model needs to be updated. In this regard, a new online method based on LSTM network is proposed in this paper. The use of online technique offers us to retrain the model as new data arrives, which helps in improving the accuracy of the estimated RUL. To illustrate the application of the proposed online LSTM method, we have used the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) turbofan dataset. The results show an improved efficiency compared to the previously proposed methods for RUL estimation.

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