scholarly journals Prognostics Comparison of Lithium-Ion Battery Based on the Shallow and Deep Neural Networks Model

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3271 ◽  
Author(s):  
Bing Long ◽  
Xiangnan Li ◽  
Xiaoyu Gao ◽  
Zhen Liu
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Short Term Memory ◽  
Back Propagation ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Battery Management ◽  
Prognostic Accuracy ◽  
Artificial Neural Network Ann ◽  
Nonlinear Autoregressive

Prognostics of the remaining useful life (RUL) of lithium-ion batteries is a crucial role in the battery management systems (BMS). An artificial neural network (ANN) does not require much knowledge from the lithium-ion battery systems, thus it is a prospective data-driven prognostic method of lithium-ion batteries. Though the ANN has been applied in prognostics of lithium-ion batteries in some references, no one has compared the prognostics of the lithium-ion batteries based on different ANN. The ANN generally can be classified to two categories: the shallow ANN, such as the back propagation (BP) ANN and the nonlinear autoregressive (NAR) ANN, and the deep ANN, such as the long short-term memory (LSTM) NN. An improved LSTM NN is proposed in order to achieve higher prediction accuracy and make the construction of the model simpler. According to the lithium-ion data from the NASA Ames, the prognostics comparison of lithium-ion battery based on the BP ANN, the NAR ANN, and the LSTM ANN was studied in detail. The experimental results show: (1) The improved LSTM ANN has the best prognostic accuracy and is more suitable for the prediction of the RUL of lithium-ion batteries compared to the BP ANN and the NAR ANN; (2) the NAR ANN has better prognostic accuracy compared to the BP ANN.

scholarly journals Comparing Single and Hybrid methods of Deep Learning for Remaining Useful Life Prediction of Lithium-ion Batteries

2021 ◽  
Vol 297 ◽  
pp. 01043
Author(s):  
Brahim Zraibi ◽  
Mohamed Mansouri ◽  
Chafik Okar
Keyword(s):  
Lithium Ion Battery ◽  
Hybrid Method ◽  
Short Term Memory ◽  
Hybrid Methods ◽  
Warning System ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Safety And Reliability ◽  
Useful Life ◽  
Statistical Indicators

The prediction lifetime of a Lithium-ion battery is able to be utilized as an early warning system to prevent the battery’s failure that makes it very significant for assuring safety and reliability. This paper represents a benchmark study that compares its RUL prediction results of single and hybrid methods with similar articles. We suggest a hybrid method, named the CNN-LSTM, which is a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), for predicting and improving the accuracy of the remaining useful life (RUL) of Lithium-ion battery. We selected three statistical indicators (MAE, R², and RMSE) to assess the results of performance prediction. Experimental validation is performed using the lithium-ion battery dataset from the NASA and results reveal that the effectiveness of the suggested hybrid method in reducing the prediction error and in achieving better RUL prediction performance compared to the other algorithms.

scholarly journals A LSTM-STW and GS-LM Fusion Method for Lithium-Ion Battery RUL Prediction Based on EEMD

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2380 ◽  
Author(s):  
Ling Mao ◽  
Jie Xu ◽  
Jiajun Chen ◽  
Jinbin Zhao ◽  
Yuebao Wu ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
High Frequency ◽  
Short Term Memory ◽  
Time Window ◽  
Low Frequency ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Marquardt Algorithm ◽  
Starting Point ◽  
Frequency Components

To address inaccurate prediction in remaining useful life (RUL) in current Lithium-ion batteries, this paper develops a Long Short-Term Memory Network, Sliding Time Window (LSTM-STW) and Gaussian or Sine function, Levenberg-Marquardt algorithm (GS-LM) fusion batteries RUL prediction method based on ensemble empirical mode decomposition (EEMD). Firstly, EEMD is used to decompose the original data into high-frequency and low-frequency components. Secondly, LSTM-STW and GS-LM are used to predict the high-frequency and low-frequency components, respectively. Finally, the LSTM-STW and GS-LM prediction results are effectively integrated in order to obtain the final prediction of the lithium-ion battery RUL results. This article takes the lithium-ion battery data published by NASA as input. The experimental results show that the method has higher accuracy, including the phenomenon of sudden capacity increase, and is less affected by the prediction starting point. The performance of the proposed method is better than other typical battery RUL prediction methods.

scholarly journals A Lithium-ion Battery RUL Prediction Method Considering the Capacity Regeneration Phenomenon

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2247 ◽  
Author(s):  
Xiaoqiong Pang ◽  
Rui Huang ◽  
Jie Wen ◽  
Yuanhao Shi ◽  
Jianfang Jia ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Health Management ◽  
Prediction Method ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Multi Scale ◽  
Battery Capacity ◽  
Rest Time ◽  
Auto Regressive

Prediction of Remaining Useful Life (RUL) of lithium-ion batteries plays a significant role in battery health management. Battery capacity is often chosen as the Health Indicator (HI) in research on lithium-ion battery RUL prediction. In the rest time of batteries, capacity will produce a certain degree of regeneration phenomenon, which exists in the use of each battery. Therefore, considering the capacity regeneration phenomenon in RUL prediction of lithium-ion batteries is helpful to improve the prediction performance of the model. In this paper, a novel method fusing the wavelet decomposition technology (WDT) and the Nonlinear Auto Regressive neural network (NARNN) model for predicting the RUL of a lithium-ion battery is proposed. Firstly, the multi-scale WDT is used to separate the global degradation and local regeneration of a battery capacity series. Then, the RUL prediction framework based on the NARNN model is constructed for the extracted global degradation and local regeneration. Finally, the two parts of the prediction results are combined to obtain the final RUL prediction result. Experiments show that the proposed method can not only effectively capture the capacity regeneration phenomenon, but also has high prediction accuracy and is less affected by different prediction starting points.

scholarly journals A Hybrid Prognostic Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Wen-An Yang ◽  
Maohua Xiao ◽  
Wei Zhou ◽  
Yu Guo ◽  
Wenhe Liao
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Learning Algorithm ◽  
Lithium Ion ◽  
Relevance Vector Machine ◽  
Remaining Useful Life ◽  
Support Vector ◽  
Failure Event ◽  
Useful Life ◽  
Data Driven Modeling

Lithium-ion battery is a core component of many systems such as satellite, spacecraft, and electric vehicles and its failure can lead to reduced capability, downtime, and even catastrophic breakdowns. Remaining useful life (RUL) prediction of lithium-ion batteries before the future failure event is extremely crucial for proactive maintenance/safety actions. This study proposes a hybrid prognostic approach that can predict the RUL of degraded lithium-ion batteries using physical laws and data-driven modeling simultaneously. In this hybrid prognostic approach, the relevant vectors obtained with the selective kernel ensemble-based relevance vector machine (RVM) learning algorithm are fitted to the physical degradation model, which is then extrapolated to failure threshold for estimating the RUL of the lithium-ion battery of interest. The experimental results indicated that the proposed hybrid prognostic approach can accurately predict the RUL of degraded lithium-ion batteries. Empirical comparisons show that the proposed hybrid prognostic approach using the selective kernel ensemble-based RVM learning algorithm performs better than the hybrid prognostic approaches using the popular learning algorithms of feedforward artificial neural networks (ANNs) like the conventional backpropagation (BP) algorithm and support vector machines (SVMs). In addition, an investigation is also conducted to identify the effects of RVM learning algorithm on the proposed hybrid prognostic approach.

scholarly journals A Data-Driven Predictive Prognostic Model for Lithium-Ion Batteries based on a Deep Learning Algorithm

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 660 ◽  
Author(s):  
Phattara Khumprom ◽  
Nita Yodo
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Learning Algorithms ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Support Vector

Prognostic and health management (PHM) can ensure that a lithium-ion battery is working safely and reliably. The main approach of PHM evaluation of the battery is to determine the State of Health (SoH) and the Remaining Useful Life (RUL) of the battery. The advancements of computational tools and big data algorithms have led to a new era of data-driven predictive analysis approaches, using machine learning algorithms. This paper presents the preliminary development of the data-driven prognostic, using a Deep Neural Networks (DNN) approach to predict the SoH and the RUL of the lithium-ion battery. The effectiveness of the proposed approach was implemented in a case study with a battery dataset obtained from the National Aeronautics and Space Administration (NASA) Ames Prognostics Center of Excellence (PCoE) database. The proposed DNN algorithm was compared against other machine learning algorithms, namely, Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Artificial Neural Networks (ANN), and Linear Regression (LR). The experimental results reveal that the performance of the DNN algorithm could either match or outweigh other machine learning algorithms. Further, the presented results could serve as a benchmark of SoH and RUL prediction using machine learning approaches specifically for lithium-ion batteries application.

scholarly journals Empirical and physics-based approaches to estimate states of lithium-ion battery

2019 ◽  
Vol 21 (2) ◽  
pp. 259-268
Author(s):  
Anna A. Fedorova
Keyword(s):  
Embedded System ◽  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Battery Management System ◽  
Battery Management ◽  
Market Demand ◽  
Huge Number ◽  
Battery Model ◽  
The Embedded System

Lithium-ion batteries are integral parts of our life due to the rapid increase of applications which require batteries for their exploitation. Thus, there is a market demand to produce lithium-ion batteries for a huge number of applications from electric vehicles to energy storages. Battery Management System (BMS) is developed to maintain safe battery exploitation conditions. Most BMSs are embedded systems that have physical memory limits. Therefore, battery model should be easy to simulate to be integrated into BMS for states estimation. In the present paper we intend to compare empirical and physics-based approaches to estimate lithium-ion battery states with respect to their possibility of implementation in the embedded system. We will use Kalman filter to estimate battery states by means of the mentioned models.

Remaining useful life prediction of lithium-ion battery based on auto-regression and particle filter

2021 ◽  
Vol 14 (2) ◽  
pp. 218-237
Author(s):  
Jie Lin ◽  
Minghua Wei
Keyword(s):  
Particle Filter ◽  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Degradation Model ◽  
Content Type ◽  
Double Exponential ◽  
Useful Life ◽  
Auto Regression

PurposeWith the rapid development and stable operated application of lithium-ion batteries used in uninterruptible power supply (UPS), the prediction of remaining useful life (RUL) for lithium-ion battery played an important role. More and more researchers paid more attentions on the reliability and safety for lithium-ion batteries based on prediction of RUL. The purpose of this paper is to predict the life of lithium-ion battery based on auto regression and particle filter method.Design/methodology/approachIn this paper, a simple and effective RUL prediction method based on the combination method of auto-regression (AR) time-series model and particle filter (PF) was proposed for lithium-ion battery. The proposed method deformed the double-exponential empirical degradation model and reduced the number of parameters for such model to improve the efficiency of training. By using the PF algorithm to track the process of lithium-ion battery capacity decline and modified observations of the state space equations, the proposed PF + AR model fully considered the declined process of batteries to meet more accurate prediction of RUL.FindingsExperiments on CALCE dataset have fully compared the conventional PF algorithm and the AR + PF algorithm both on original exponential empirical degradation model and the deformed double-exponential one. Experimental results have shown that the proposed PF + AR method improved the prediction accuracy, decreases the error rate and reduces the uncertainty ranges of RUL, which was more suitable for the deformed double-exponential empirical degradation model.Originality/valueIn the running of UPS device based on lithium-ion battery, the proposed AR + PF combination algorithm will quickly, accurately and robustly predict the RUL of lithium-ion batteries, which had a strong application value in the stable operation of laboratory and other application scenarios.

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