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

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

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331220961576 ◽

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.

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Diagnostics and Prognostics of Lithium-Ion Batteries

Volume 2A: 41st Design Automation Conference ◽

10.1115/detc2015-46935 ◽

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.

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Dynamic Long Short-Term Memory Neural-Network- Based Indirect Remaining-Useful-Life Prognosis for Satellite Lithium-Ion Battery

Applied Sciences ◽

10.3390/app8112078 ◽

2018 ◽

Vol 8 (11) ◽

pp. 2078 ◽

Cited By ~ 18

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.

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