Model-based State-of-charge Estimation Approach of the Lithium-ion Battery Using an Improved Adaptive Particle Filter

Min Ye; Hui Guo; Rui Xiong; Ruixin Yang

doi:10.1016/j.egypro.2016.11.305

Model-based State-of-charge Estimation Approach of the Lithium-ion Battery Using an Improved Adaptive Particle Filter

Min Ye, Hui Guo, Rui Xiong^*, Ruixin Yang

^*Corresponding author for this work

School of Mechanical Engineering

Chang'an University

Research output: Contribution to journal › Conference article › peer-review

25 Citations (Scopus)

Abstract

Accurate state of charge (SoC) estimation is of great significance for a lithium-ion battery. This paper presents an adaptive particle filter (APF)-based SoC estimation algorithm for lithium-ion batteries in electric vehicles. Firstly, the lithium-ion battery is modeled using the resistance-capacitance network based one-state hysteresis equivalent circuit model and its parameters are determined by the particle swarm optimization method. Then, an improved adaptive particle filter has been proposed and applied to the battery SoC estimation. Finally, the two typical lithium-ion battery, LiFePO4 and NMC lithium-ion, have been used to verify the proposed SoC estimator.

Original language	English
Pages (from-to)	394-399
Number of pages	6
Journal	Energy Procedia
Volume	103
DOIs	https://doi.org/10.1016/j.egypro.2016.11.305
Publication status	Published - 1 Dec 2016
Event	Applied Energy Symposium and Submit: Renewable Energy Integration with Mini/Microgrid, REM 2016 - Maldives, Maldives Duration: 19 Apr 2016 → 21 Apr 2016

Keywords

APF
Battery management system
PSO
improved APF

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.egypro.2016.11.305

Cite this

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title = "Model-based State-of-charge Estimation Approach of the Lithium-ion Battery Using an Improved Adaptive Particle Filter",

abstract = "Accurate state of charge (SoC) estimation is of great significance for a lithium-ion battery. This paper presents an adaptive particle filter (APF)-based SoC estimation algorithm for lithium-ion batteries in electric vehicles. Firstly, the lithium-ion battery is modeled using the resistance-capacitance network based one-state hysteresis equivalent circuit model and its parameters are determined by the particle swarm optimization method. Then, an improved adaptive particle filter has been proposed and applied to the battery SoC estimation. Finally, the two typical lithium-ion battery, LiFePO4 and NMC lithium-ion, have been used to verify the proposed SoC estimator.",

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AU - Ye, Min

AU - Guo, Hui

AU - Xiong, Rui

AU - Yang, Ruixin

PY - 2016/12/1

Y1 - 2016/12/1

N2 - Accurate state of charge (SoC) estimation is of great significance for a lithium-ion battery. This paper presents an adaptive particle filter (APF)-based SoC estimation algorithm for lithium-ion batteries in electric vehicles. Firstly, the lithium-ion battery is modeled using the resistance-capacitance network based one-state hysteresis equivalent circuit model and its parameters are determined by the particle swarm optimization method. Then, an improved adaptive particle filter has been proposed and applied to the battery SoC estimation. Finally, the two typical lithium-ion battery, LiFePO4 and NMC lithium-ion, have been used to verify the proposed SoC estimator.

AB - Accurate state of charge (SoC) estimation is of great significance for a lithium-ion battery. This paper presents an adaptive particle filter (APF)-based SoC estimation algorithm for lithium-ion batteries in electric vehicles. Firstly, the lithium-ion battery is modeled using the resistance-capacitance network based one-state hysteresis equivalent circuit model and its parameters are determined by the particle swarm optimization method. Then, an improved adaptive particle filter has been proposed and applied to the battery SoC estimation. Finally, the two typical lithium-ion battery, LiFePO4 and NMC lithium-ion, have been used to verify the proposed SoC estimator.

KW - APF

KW - Battery management system

KW - PSO

KW - improved APF

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DO - 10.1016/j.egypro.2016.11.305

M3 - Conference article

AN - SCOPUS:85010878079

SN - 1876-6102

VL - 103

SP - 394

EP - 399

JO - Energy Procedia

JF - Energy Procedia

T2 - Applied Energy Symposium and Submit: Renewable Energy Integration with Mini/Microgrid, REM 2016

Y2 - 19 April 2016 through 21 April 2016

ER -

Model-based State-of-charge Estimation Approach of the Lithium-ion Battery Using an Improved Adaptive Particle Filter

Abstract

Keywords

UN SDGs

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