A novel multi-model probability based battery stateofcharge fusion estimation approach

Hao Mu; Rui Xiong; Fengchun Sun

doi:10.1016/j.egypro.2016.06.061

A novel multi-model probability based battery stateofcharge fusion estimation approach

Hao Mu, Rui Xiong^*, Fengchun Sun

^*Corresponding author for this work

School of Mechanical Engineering

Beijing Institute of Technology

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

6 Citations (Scopus)

Abstract

Accurate state of charge (SoC) estimation is very important for managing battery with safety and efficiency. In order to improve the reliability and redundancy of the SoC estimation, the multi-model probability fusion estimation (MMPFE) method is presented. Considering that the estimation results being dependent on models, the MMPFE method is utilized to fuse the SoC results gained by different equivalent circuit models (ECMs). LFP type battery are tested to verify the effectiveness of the method. Results indicate that the proposed approach can achieve accurate battery SoC estimation with good robust and reliability.

Original language	English
Pages (from-to)	840-846
Number of pages	7
Journal	Energy Procedia
Volume	88
DOIs	https://doi.org/10.1016/j.egypro.2016.06.061
Publication status	Published - 1 Jun 2016
Event	Applied Energy Symposium and Summit on Low-Carbon Cities and Urban Energy Systems, CUE 2015 - Fuzhou, China Duration: 15 Nov 2015 → 17 Nov 2015

Keywords

Batteries
Electric vehicles
H infinity
Multi-model probability
State estimation

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10.1016/j.egypro.2016.06.061

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title = "A novel multi-model probability based battery stateofcharge fusion estimation approach",

abstract = "Accurate state of charge (SoC) estimation is very important for managing battery with safety and efficiency. In order to improve the reliability and redundancy of the SoC estimation, the multi-model probability fusion estimation (MMPFE) method is presented. Considering that the estimation results being dependent on models, the MMPFE method is utilized to fuse the SoC results gained by different equivalent circuit models (ECMs). LFP type battery are tested to verify the effectiveness of the method. Results indicate that the proposed approach can achieve accurate battery SoC estimation with good robust and reliability.",

keywords = "Batteries, Electric vehicles, H infinity, Multi-model probability, State estimation",

author = "Hao Mu and Rui Xiong and Fengchun Sun",

note = "Publisher Copyright: {\textcopyright} 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.; Applied Energy Symposium and Summit on Low-Carbon Cities and Urban Energy Systems, CUE 2015 ; Conference date: 15-11-2015 Through 17-11-2015",

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doi = "10.1016/j.egypro.2016.06.061",

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TY - JOUR

T1 - A novel multi-model probability based battery stateofcharge fusion estimation approach

AU - Mu, Hao

AU - Xiong, Rui

AU - Sun, Fengchun

PY - 2016/6/1

Y1 - 2016/6/1

N2 - Accurate state of charge (SoC) estimation is very important for managing battery with safety and efficiency. In order to improve the reliability and redundancy of the SoC estimation, the multi-model probability fusion estimation (MMPFE) method is presented. Considering that the estimation results being dependent on models, the MMPFE method is utilized to fuse the SoC results gained by different equivalent circuit models (ECMs). LFP type battery are tested to verify the effectiveness of the method. Results indicate that the proposed approach can achieve accurate battery SoC estimation with good robust and reliability.

AB - Accurate state of charge (SoC) estimation is very important for managing battery with safety and efficiency. In order to improve the reliability and redundancy of the SoC estimation, the multi-model probability fusion estimation (MMPFE) method is presented. Considering that the estimation results being dependent on models, the MMPFE method is utilized to fuse the SoC results gained by different equivalent circuit models (ECMs). LFP type battery are tested to verify the effectiveness of the method. Results indicate that the proposed approach can achieve accurate battery SoC estimation with good robust and reliability.

KW - Batteries

KW - Electric vehicles

KW - H infinity

KW - Multi-model probability

KW - State estimation

UR - http://www.scopus.com/inward/record.url?scp=85007560349&partnerID=8YFLogxK

U2 - 10.1016/j.egypro.2016.06.061

DO - 10.1016/j.egypro.2016.06.061

M3 - Conference article

AN - SCOPUS:85007560349

SN - 1876-6102

VL - 88

SP - 840

EP - 846

JO - Energy Procedia

JF - Energy Procedia

T2 - Applied Energy Symposium and Summit on Low-Carbon Cities and Urban Energy Systems, CUE 2015

Y2 - 15 November 2015 through 17 November 2015

ER -

A novel multi-model probability based battery stateofcharge fusion estimation approach

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Keywords

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