scholarly journals Prediction of remaining useful life for lithium-ion battery with multiple health indicators

2021 ◽  
Vol 23 (1) ◽  
pp. 176-183
Author(s):  
Chun Su ◽  
Hongjing Chen ◽  
Zejun Wen
Keyword(s):  
Lithium Ion ◽  
Health Indicators ◽  
Primary Energy ◽  
Remaining Useful Life ◽  
Generalized Regression Neural Network ◽  
Current Voltage ◽  
Remaining Capacity ◽  
Li Ion ◽  
Useful Life ◽  
Multiple Health

Lithium-ion (Li-ion) battery has become a primary energy form for a variety of engineering equipments. To ensure the equipments’ reliability, it is crucial to accurately predict Liion battery’s remaining capacity as well as its remaining useful life (RUL). In this study, we propose a novel method for Li-ion battery’s online RUL prediction, which is based on multiple health indicators (HIs) and can be derived from the battery’s historical operation data. Firstly, four types of indirect HIs are built according to the battery’s operation current, voltage and temperature data respectively. On this basis, a generalized regression neural network (GRNN) is presented to estimate the battery’s remaining capacity, and the nonlinear autoregressive approach (NAR) is applied to predict the battery’s RUL based on the estimated capacity value. Furthermore, to reduce the interference, twice wavelet denoising are performed with different thresholds. A case study is conducted with a NASA battery dataset to demonstrate the effectiveness of the method. The result shows that the proposed method can obtain Li-ion batteries’ RUL effectively.

scholarly journals Prediction of Remaining Useful Life for Lithium-Ionbattery with Multiple Health Indicators

2020 ◽  
Author(s):  
Yuqiao Zheng ◽  
Hong jing Chen ◽  
Chun Su
Keyword(s):  
Prediction Method ◽  
Health Indicators ◽  
Remaining Useful Life ◽  
Generalized Regression Neural Network ◽  
Li Ion Battery ◽  
Current Voltage ◽  
Remaining Capacity ◽  
Li Ion ◽  
Online Prediction ◽  
Multiple Health

The remaining capacity can only be measured with offline method. This brings great challenge for the online prediction of Li-ion battery’s RUL. A novel online prediction method for Li-ion battery’s RUL was proposed, which is based on multiple health indicators (HIs) and can be derived from the batteries’ historical operation data. Firstly, four indirect HIs were built according to the battery’s operation current, voltage and temperature data respectively. On that basis, a generalized regression neural network (GRNN) was developed to estimate the battery’s remaining capacity, and the non-linear autoregressive approach (NAR) was utilized to predict the battery’s RUL based on the estimated capacity value. Furthermore, to reduce the interference, twice wavelet denoising were performed with different thresholds. A case study is conducted with a NASA battery dataset to demonstrate the effectiveness of the method. The result shows that the proposed method can obtain Li-ion batteries’ RUL effectively.

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 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.

Comparison of Prognostic Algorithms Based on PF and EKF for Estimating Remaining Useful Life of Li-Ion Batteries

2014 ◽  
Vol 529 ◽  
pp. 616-620
Author(s):  
Jin Zhang ◽  
An Tong Gao ◽  
Wen Bing Wang ◽  
Yu Sheng Han ◽  
Rong Gang Chen ◽  
...  
Keyword(s):  
Remaining Useful Life ◽  
Lumped Parameter Model ◽  
Temperature Cycle ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Batteries ◽  
Lumped Parameter ◽  
Remaining Capacity ◽  
Li Ion ◽  
Useful Life

To estimate remaining useful life (RUL) of Li-ion batteries is a key factor for correct and safe battery management, particularly for the development of Battery Management System (BMS). A lumped parameter model which integrates the non-linear open-circuit voltage, current, temperature, cycle number, and remaining capacity and other dynamic characteristics is created based on the battery electrical characteristics is presented. A particle filter (PF) algorithm which syncretizes Li-ion battery electrochemical working process is proposed according to the sequence importance of re-sampling to predict its discharge end time in single cycle time and cycle life. Besides, for comparison, a extended Kalman filter (EKF) algorithm is also proposed to estimate the RUL based on the same statistics. Simulation results show that the PF algorithm according to lumped parameter model has a better precision in estimating RUL compared with the EKF algorithm.

scholarly journals A Hybrid Method for Remaining Useful Life Estimation of Lithium-Ion Battery with Regeneration Phenomena

2019 ◽  
Vol 9 (9) ◽  
pp. 1890 ◽  
Author(s):  
Lin Zhao ◽  
Yipeng Wang ◽  
Jianhua Cheng
Keyword(s):  
Lithium Ion Battery ◽  
Prediction Accuracy ◽  
Lithium Ion ◽  
Degradation Process ◽  
Electronic System ◽  
Primary Energy ◽  
Remaining Useful Life ◽  
Training Dataset ◽  
Capacity Degradation ◽  
Useful Life

The lithium-ion battery has become the primary energy source of many electronic devices. Accurately forecasting the remaining useful life (RUL) of a battery plays an essential role in ensuring reliable operatioin of an electronic system. This paper investigates the lithium-ion battery RUL prediction problem with capacity regeneration phenomena. We aim to reduce the accumulation of the prediction error by integrating different capacity degradation models and thereby improve the prediction accuracy of the long-term RUL. To describe the degradation process more accurately, we decoupled the degradation process into two types: capacity regeneration and normal degradation. Then, we modelled two kinds of degradation processes separately. In the prediction phase, we predicted the battery state of health (SOH) by using the relevance vector machine (RVM) and the gray model (GM) alternately, updated the training dataset according to the prediction results, and then updated the RVM and GM. The RVM and GM correct each other’s prediction results constantly, which reduces the cumulative error of prediction and improves the prediction accuracy of the battery SOH. Experimental results with the National Aeronautics and Space Administration (NASA) battery dataset demonstrated that the proposed method can accurately establish the degradation model and achieve better performance for the RUL estimation as compared with the single RVM or GM methods.

Remaining Useful Life Prediction of Lithium-ion Batteries Using Multiple Kernel Extreme Learning Machine

2020 ◽  
Vol 13 ◽  
Author(s):  
Renxiong Liu
Keyword(s):  
Lithium Ion Batteries ◽  
Extreme Learning Machine ◽  
Kernel Function ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Prediction Methods ◽  
Multiple Kernel ◽  
Kernel Extreme Learning Machine ◽  
Useful Life ◽  
Learning Machine

Objective: Lithium-ion batteries are important components used in electric automobiles (EVs), fuel cell EVs and other hybrid EVs. Therefore, it is greatly important to discover its remaining useful life (RUL). Methods: In this paper, a battery RUL prediction approach using multiple kernel extreme learning machine (MKELM) is presented. The MKELM’s kernel keeps diversified by consisting multiple kernel functions including Gaussian kernel function, Polynomial kernel function and Sigmoid kernel function, and every kernel function’s weight and parameter are optimized through differential evolution (DE) algorithm. Results : Battery capacity data measured from NASA Ames Prognostics Center are used to demonstrate the prediction procedure of the proposed approach, and the MKELM is compared with other commonly used prediction methods in terms of absolute error, relative accuracy and mean square error. Conclusion: The prediction results prove that the MKELM approach can accurately predict the battery RUL. Furthermore, a compare experiment is executed to validate that the MKELM method is better than other prediction methods in terms of prediction accuracy.

A data-driven intelligent hybrid method for health prognosis of lithium-ion batteries

2021 ◽  
pp. 1-11
Author(s):  
Vimal Singh Bisht ◽  
Mashhood Hasan ◽  
Hasmat Malik ◽  
Sandeep Sunori
Keyword(s):  
Health Assessment ◽  
Lithium Ion ◽  
Transformation Method ◽  
Internal Resistance ◽  
Remaining Useful Life ◽  
Health Index ◽  
Great Saving ◽  
Useful Life ◽  
And Storage ◽  
Value Of Health

For estimation of the RUL (Remaining useful life) of Lithium ion battery we are required to do its health assessment using online facilities. For identifying the health of a battery its internal resistance and storage capacity plays the major role. However the estimation of both these parameters is not an easy job and requires lot of computational work to be done. So to overcome this constraint an easy alternate way is simulated in the paper through which we can estimate the RUL. For formation of a linear relationship between health index of the battery (HI) and its actual capacity used of power transformation method is done and later on to validate the result a comparison study is done with Pearson & Spearman methods. Transformed value of Health Index is used for developing a neural network. The results demonstrated in the paper shows the feasibility of the proposed technique resulting in great saving of time

scholarly journals Remaining useful life prediction for lithium-ion battery by combining an improved particle filter with sliding-window gray model

2020 ◽  
Vol 6 ◽  
pp. 2086-2093
Author(s):  
Lin Chen ◽  
Jingjing An ◽  
Huimin Wang ◽  
Mo Zhang ◽  
Haihong Pan
Keyword(s):  
Particle Filter ◽  
Lithium Ion Battery ◽  
Life Prediction ◽  
Lithium Ion ◽  
Sliding Window ◽  
Remaining Useful Life ◽  
Gray Model ◽  
Useful Life
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