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.

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.

State of Health Estimation of Lithium-Ion Batteries Based on Combination of Gaussian Distribution Data and Least Squares Support Vector Machines Regression

2018 ◽  
Vol 929 ◽  
pp. 93-102
Author(s):  
Didik Djoko Susilo ◽  
Achmad Widodo ◽  
Toni Prahasto ◽  
Muhammad Nizam
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Support Vector ◽  
Degradation Data ◽  
Starting Point ◽  
Capacity Degradation ◽  
Battery Capacity ◽  
Useful Life

Lithium-ion batteries play a critical role in the reliability and safety of a system. Battery health monitoring and remaining useful life (RUL) prediction are needed to prevent catastrophic failure of the battery. The aim of this research is to develop a data-driven method to monitor the batteries state of health and predict their RUL by using the battery capacity degradation data. This paper also investigated the effect of prediction starting point to the RUL prediction error. One of the data-driven method drawbacks is the need of a large amount of data to obtain accurate prediction. This paper proposed a method to generate a series of degradation data that follow the Gaussian distribution based on limited battery capacity degradation data. The prognostic model was constructed from the new data using least square support vector machine (LSSVM) regression. The remaining useful life prediction was carried out by extrapolating the model until reach the end of life threshold. The method was applied to three differences lithium-ion batteries capacity data. The results showed that the proposed method has good performance. The method can predict the lithium-ion batteries RUL with a small error, and the optimal RUL starting point was found at the point where the battery has experienced the highest capacity recovery due to the self-recharge phenomenon.

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

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3678 ◽  
Author(s):  
Tianfei Sun ◽  
Bizhong Xia ◽  
Yifan Liu ◽  
Yongzhi Lai ◽  
Weiwei Zheng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Particle Filter ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Model Based ◽  
Capacity Degradation ◽  
Battery Capacity ◽  
Useful Life ◽  
Learning Machine

The prognosis of lithium-ion batteries for their remaining useful life is an essential technology in prognostics and health management (PHM). In this paper, we propose a novel hybrid prediction method based on particle filter (PF) and extreme learning machine (ELM). First, we use ELM to simulate the battery capacity degradation trend. Second, PF is applied to update the random parameters of the ELM in real-time. An extreme learning machine prognosis model, based on particle filter (PFELM), is established. In order to verify the validity of this method, our proposed approach is compared with the standard ELM, the multi-layer perceptron prediction model, based on PF (PFMLP), as well as the neural network prediction model, based on bat-particle filter (BATPFNN), using the batteries testing datasets of the National Aeronautics and Space Administration (NASA) Ames Research Center. The results show that our proposed approach has better ability to simulate battery capacity degradation trends, better robustness, and higher Remaining Useful Life (RUL) prognosis accuracy than the standard ELM, the PFMLP, and the BATPFNN under the same conditions.

scholarly journals A Method for Predicting the Remaining Useful Life of Lithium-Ion Batteries Based on Particle Filter Using Kendall Rank Correlation Coefficient

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4183
Author(s):  
Diju Gao ◽  
Yong Zhou ◽  
Tianzhen Wang ◽  
Yide Wang
Keyword(s):  
Particle Filter ◽  
Lithium Ion Batteries ◽  
Correlation Coefficient ◽  
Lithium Batteries ◽  
Rank Correlation ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Kendall Rank Correlation ◽  
Battery Capacity ◽  
Useful Life

With the wide application of lithium batteries, battery fault prediction and health management have become more and more important. This article proposes a method for predicting the remaining useful life (RUL) of lithium-ion batteries to avoid a series of safety problems caused by continuing to use the battery after reaching its service life threshold. Since the battery capacity is not easy to obtain online, we propose that some measurable parameters should be used in the battery discharge cycle to estimate battery capacity. Then, the estimated capacity is used to replace the measured value of the particle filter (PF) based on the Kendall rank correlation coefficient (KCCPF) to predict the RUL of the lithium batteries. Simulation results show that the proposed method has high prediction accuracy, stability, and practical value.

Prognostics of lithium-ion batteries based on different dimensional state equations in the particle filtering method

2016 ◽  
Vol 39 (10) ◽  
pp. 1537-1546 ◽  
Author(s):  
Xiaohong Su ◽  
Shuai Wang ◽  
Michael Pecht ◽  
Peijun Ma ◽  
Lingling Zhao
Keyword(s):  
Lithium Ion Batteries ◽  
Particle Filtering ◽  
Prediction Accuracy ◽  
Lithium Ion ◽  
Exponential Model ◽  
State Equation ◽  
Remaining Useful Life ◽  
State Equations ◽  
Battery Capacity ◽  
Useful Life

Accurate prediction of the remaining useful life of lithium-ion batteries plays a significant role in various devices and many researchers have focused on lithium-ion battery reliability and prognosis. A particle filter (PF) is an effective filter for estimation and prediction of time series data where model structure is available. The prediction accuracy of a PF depends on two key factors: parameter initialization and the state equation. In this paper, parameters are estimated using a PF and two empirical exponential models, i.e. the exponential model and improved exponential model, are used to track the battery capacity degradation; each model uses a different state equation. Experiments were performed to compare prediction accuracy using the related parameters estimation model with that using the capacity decline model; this paper compares the effects of the different state equations on the lithium-ion battery remaining useful life prediction. The experimental results show the merits of the capacity decline model based on particle filtering. The capacity decline model PF is more suitable for estimating the battery capacity trend in the long term.

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