Modeling, Evaluation, and State Estimation for Batteries

Hao Mu; Rui Xiong

doi:10.1016/B978-0-12-812786-5.00001-X

Modeling, Evaluation, and State Estimation for Batteries

Hao Mu^*
, Rui Xiong

^*Corresponding author for this work

School of Mechanical Engineering

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

10 Citations (Scopus)

Abstract

State estimation of the lithium-ion battery has been the focus of many researchers, and the consensus is that the model-based method is an effective tool for state of charge (SoC) estimation. In this chapter, we start with battery modeling. Several modeling approaches are presented and the their advantages and disadvantages are discussed. Moreover, the balance problem between model accuracy and complexity of an nth order RC networks model is tackled using an evaluation index of terminal voltages. Finally, the adaptive extended Kalman filter algorithm is proposed to estimate the SoC and its validity is confirmed.

Original language	English
Title of host publication	Modeling, Dynamics, and Control of Electrified Vehicles
Publisher	Elsevier Inc.
Pages	1-38
Number of pages	38
ISBN (Electronic)	9780128131091
ISBN (Print)	9780128127865
DOIs	https://doi.org/10.1016/B978-0-12-812786-5.00001-X
Publication status	Published - 2018

Keywords

AEKF algorithm
Dual timescales
Electric vehicles
Evaluation of models
Lithium-ion battery
Modeling
State of charge

UN SDGs

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

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10.1016/B978-0-12-812786-5.00001-X

Cite this

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abstract = "State estimation of the lithium-ion battery has been the focus of many researchers, and the consensus is that the model-based method is an effective tool for state of charge (SoC) estimation. In this chapter, we start with battery modeling. Several modeling approaches are presented and the their advantages and disadvantages are discussed. Moreover, the balance problem between model accuracy and complexity of an nth order RC networks model is tackled using an evaluation index of terminal voltages. Finally, the adaptive extended Kalman filter algorithm is proposed to estimate the SoC and its validity is confirmed.",

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T1 - Modeling, Evaluation, and State Estimation for Batteries

AU - Mu, Hao

AU - Xiong, Rui

PY - 2018

Y1 - 2018

N2 - State estimation of the lithium-ion battery has been the focus of many researchers, and the consensus is that the model-based method is an effective tool for state of charge (SoC) estimation. In this chapter, we start with battery modeling. Several modeling approaches are presented and the their advantages and disadvantages are discussed. Moreover, the balance problem between model accuracy and complexity of an nth order RC networks model is tackled using an evaluation index of terminal voltages. Finally, the adaptive extended Kalman filter algorithm is proposed to estimate the SoC and its validity is confirmed.

AB - State estimation of the lithium-ion battery has been the focus of many researchers, and the consensus is that the model-based method is an effective tool for state of charge (SoC) estimation. In this chapter, we start with battery modeling. Several modeling approaches are presented and the their advantages and disadvantages are discussed. Moreover, the balance problem between model accuracy and complexity of an nth order RC networks model is tackled using an evaluation index of terminal voltages. Finally, the adaptive extended Kalman filter algorithm is proposed to estimate the SoC and its validity is confirmed.

KW - AEKF algorithm

KW - Dual timescales

KW - Electric vehicles

KW - Evaluation of models

KW - Lithium-ion battery

KW - Modeling

KW - State of charge

UR - https://www.scopus.com/pages/publications/85072790451

U2 - 10.1016/B978-0-12-812786-5.00001-X

DO - 10.1016/B978-0-12-812786-5.00001-X

M3 - Chapter

AN - SCOPUS:85042455864

SN - 9780128127865

SP - 1

EP - 38

BT - Modeling, Dynamics, and Control of Electrified Vehicles

PB - Elsevier Inc.

ER -

Modeling, Evaluation, and State Estimation for Batteries

Abstract

Keywords

UN SDGs

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