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 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.

Analysis of Data-Driven Prediction Algorithms for Lithium-Ion Batteries Remaining Useful Life

2013 ◽  
Vol 717 ◽  
pp. 390-395 ◽  
Author(s):  
Lin Jiang ◽  
Wei Ming Xian ◽  
Bin Long ◽  
Hou Jun Wang
Keyword(s):  
Lithium Ion Batteries ◽  
Particle Filtering ◽  
Linear Trend ◽  
Lithium Ion ◽  
Single Step ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Support Vector ◽  
Data Set ◽  
Useful Life

As one of the most widely used energy storage systems, lithium-ion batteries are attracting more and more attention, and the estimation of lithium-ion batteries remaining useful life (RUL) becoming a critical problem. Generally, RUL can be predicted in two ways: physics of failure (PoF) method and data driven method. Due to the internal electro-chemical reactions are either inaccessible to sensors or hard to measure; the data-driven method is adopted because it does not require specific knowledge of material properties. In this paper, three data-driven algorithms, i.e., Support Vector Machine (SVM), Autoregressive Moving Average (ARMA), and Particle Filtering (PF) are presented for RUL prediction. The lithium-ion battery aging experiment data set has been trained to implement simulation. Based on the RUL prediction result, we can conclude that: (1) ARMA model achieved better result than SVM, however, the result shows a linear trend, which fail to properly reflect the degradation trend of the battery; (2) SVM often suffers from over fitting problem and is more suitable for single-step prediction; and (3) PF approach achieved a better prediction and reflected the trends of degradation of the battery owing to its combined with specific model.

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.

Diagnostics and Prognostics of Lithium-Ion Batteries

2015 ◽  
Author(s):  
Zhimin Xi ◽  
Rong Jing ◽  
Cheol Lee
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
State Of Charge ◽  
Data Driven ◽  
State Of Health ◽  
Li Ion Batteries ◽  
Battery State Of Charge ◽  
Li Ion ◽  
Useful Life

This paper investigates recent research on battery diagnostics and prognostics especially for Lithium-ion (Li-ion) batteries. Battery diagnostics focuses on battery models and diagnosis algorithms for battery state of charge (SOC) and state of health (SOH) estimation. Battery prognostics elaborates data-driven prognosis algorithms for predicting the remaining useful life (RUL) of battery SOC and SOH. Readers will learn not only basics but also very recent research developments on battery diagnostics and prognostics.

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.

Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods

2013 ◽  
Vol 239 ◽  
pp. 680-688 ◽  
Author(s):  
Adnan Nuhic ◽  
Tarik Terzimehic ◽  
Thomas Soczka-Guth ◽  
Michael Buchholz ◽  
Klaus Dietmayer
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Useful Life ◽  
Using Data ◽  
Health Diagnosis

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.

Data Driven Prognostics With Lack of Training Data Sets

2015 ◽  
Author(s):  
Zhimin Xi ◽  
Xiangxue Zhao
Keyword(s):  
Lithium Ion ◽  
Remaining Useful Life ◽  
Training Data ◽  
Data Driven ◽  
Data Sets ◽  
The Neural Network ◽  
Typical Data ◽  
Capacity Degradation ◽  
Battery Capacity ◽  
Network Similarity

Data-driven prognostics typically requires sufficient offline training data sets for accurate remaining useful life (RUL) prediction of engineering products. This paper investigates performances of typical data-driven methodologies when the amount of training data sets is insufficient. The purpose is to better understand these methodologies especially when offline training datasets are insufficient. The neural network, similarity-based approach, and copula-based sampling approach were investigated when only three run-to-failure training units were available. The example of lithium-ion (Li-ion) battery capacity degradation was employed for the demonstration.

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