Next Article in Journal
A Comprehensive Review on Grid Connected Photovoltaic Inverters, Their Modulation Techniques, and Control Strategies
Previous Article in Journal
Multi-Horizon Forecasting of Global Horizontal Irradiance Using Online Gaussian Process Regression: A Kernel Study
Open AccessArticle

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

by Diju Gao 1, Yong Zhou 1,*, Tianzhen Wang 1 and Yide Wang 1,2
1
Key Laboratory Marine Technology and Control Engineering, Ministry of Transport, Shanghai Maritime University, Shanghai 201306, China
2
Institut d’Électronique et des Technologies du numéRique, UMR CNRS 6164, Universite de Nantes, F-44000 Nantes, France
*
Author to whom correspondence should be addressed.
Energies 2020, 13(16), 4183; https://doi.org/10.3390/en13164183
Received: 3 July 2020 / Revised: 10 August 2020 / Accepted: 11 August 2020 / Published: 13 August 2020
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. View Full-Text
Keywords: lithium-ion battery; particle filter (PF); remaining useful life (RUL); NARX neural network lithium-ion battery; particle filter (PF); remaining useful life (RUL); NARX neural network
Show Figures

Graphical abstract

MDPI and ACS Style

Gao, D.; Zhou, Y.; Wang, T.; Wang, Y. A Method for Predicting the Remaining Useful Life of Lithium-Ion Batteries Based on Particle Filter Using Kendall Rank Correlation Coefficient. Energies 2020, 13, 4183.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop