Remaining capacity estimation of Li-ion batteries based on temperature sample entropy and particle filter

2014 ◽  
Vol 268 ◽  
pp. 895-903 ◽  
Author(s):  
Junfu Li ◽  
Chao Lyu ◽  
Lixin Wang ◽  
Liqiang Zhang ◽  
Chenhui Li
Keyword(s):  
Particle Filter ◽  
Sample Entropy ◽  
Li Ion Batteries ◽  
Capacity Estimation ◽  
Remaining Capacity ◽  
Li Ion ◽  
Temperature Sample

Discharge capacity estimation for Li-ion batteries based on particle filter under multi-operating conditions

Energy ◽  
2015 ◽  
Vol 86 ◽  
pp. 638-648 ◽  
Author(s):  
Junfu Li ◽  
Lixin Wang ◽  
Chao Lyu ◽  
Liqiang Zhang ◽  
Han Wang
Keyword(s):  
Particle Filter ◽  
Discharge Capacity ◽  
Operating Conditions ◽  
Li Ion Batteries ◽  
Capacity Estimation ◽  
Li Ion

Capacity estimation algorithm with a second-order differential voltage curve for Li-ion batteries with NMC cathodes

Energy ◽  
2017 ◽  
Vol 135 ◽  
pp. 257-268 ◽  
Author(s):  
Taedong Goh ◽  
Minjun Park ◽  
Minhwan Seo ◽  
Jun Gu Kim ◽  
Sang Woo Kim
Keyword(s):  
Estimation Algorithm ◽  
Second Order ◽  
Li Ion Batteries ◽  
Capacity Estimation ◽  
Differential Voltage ◽  
Voltage Curve ◽  
Li Ion

Experimental verification of lithium-ion battery prognostics based on an interacting multiple model particle filter

2020 ◽  
pp. 014233122096157
Author(s):  
Shuai Wang ◽  
Wei Han ◽  
Lifei Chen ◽  
Xiaochen Zhang ◽  
Michael Pecht
Keyword(s):  
Particle Filter ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Li Ion Batteries ◽  
Model Particle ◽  
Capacity Degradation ◽  
Battery Capacity ◽  
Li Ion

A new data-driven prognostic method based on an interacting multiple model particle filter (IMMPF) is proposed for use in the determination of the remaining useful life (RUL) of lithium-ion (Li-ion) batteries and the probability distribution function (PDF) of the uncertainty associated with the RUL. An IMMPF is applied to different state equations. The battery capacity degradation model is very important in the prediction of the RUL of Li-ion batteries. The IMMPF method is applied to the estimation of the RUL of Li-ion batteries using the three improved models. Three case studies are provided to validate the proposed method. The experimental results show that the one-dimensional state equation particle filter (PF) is more suitable for estimating the trend of battery capacity in the long term. The proposed method involving interacting multiple models demonstrated a stable and high prediction accuracy, as well as the capability to narrow the uncertainty in the PDF of the RUL prediction for Li-ion batteries.

scholarly journals Artificial Fish Swarm Algorithm-Based Particle Filter for Li-Ion Battery Life Prediction

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Ye Tian ◽  
Chen Lu ◽  
Zili Wang ◽  
Laifa Tao
Keyword(s):  
Particle Filter ◽  
Lithium Ion ◽  
Integrated Approach ◽  
Remaining Useful Life ◽  
Battery Life ◽  
Li Ion Batteries ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Li Ion ◽  
Swarm Algorithm

An intelligent online prognostic approach is proposed for predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries based on artificial fish swarm algorithm (AFSA) and particle filter (PF), which is an integrated approach combining model-based method with data-driven method. The parameters, used in the empirical model which is based on the capacity fade trends of Li-ion batteries, are identified dependent on the tracking ability of PF. AFSA-PF aims to improve the performance of the basic PF. By driving the prior particles to the domain with high likelihood, AFSA-PF allows global optimization, prevents particle degeneracy, thereby improving particle distribution and increasing prediction accuracy and algorithm convergence. Data provided by NASA are used to verify this approach and compare it with basic PF and regularized PF. AFSA-PF is shown to be more accurate and precise.

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