The Estimation of State of Charge for Power Battery Packs used in Hybrid Electric Vehicle

Shanshan Xie; Rui Xiong; Yongzhi Zhang; Hongwen He

doi:10.1016/j.egypro.2017.03.774

The Estimation of State of Charge for Power Battery Packs used in Hybrid Electric Vehicle

Shanshan Xie, Rui Xiong, Yongzhi Zhang, Hongwen He^*

^*Corresponding author for this work

School of Mechanical Engineering

Beijing Institute of Technology

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

17 Citations (Scopus)

Abstract

The estimation of state of charge (SOC) for power battery packs is significant for a hybrid electric vehicle with providing data supports for the efficient and fine energy managements. In this brief, extended Kalman filter (EKF), multi-model extended Kalman filter (MMEKF) and adaptive fading extended Kalman filter (AFEKF) are used to estimate SOC respectively, then they are combined with a switching strategy to match their own features with that of SOC in different working areas. The estimation error of SOC is below 2.5 percent. And the strategy is verified to have good initialization stabilities and convergence behaviors.

Original language	English
Pages (from-to)	2678-2683
Number of pages	6
Journal	Energy Procedia
Volume	105
DOIs	https://doi.org/10.1016/j.egypro.2017.03.774
Publication status	Published - 2017
Event	8th International Conference on Applied Energy, ICAE 2016 - Beijing, China Duration: 8 Oct 2016 → 11 Oct 2016

Keywords

Extended Kalman filter
Hybrid electric bus
Power battery pack
State of charge

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10.1016/j.egypro.2017.03.774

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title = "The Estimation of State of Charge for Power Battery Packs used in Hybrid Electric Vehicle",

abstract = "The estimation of state of charge (SOC) for power battery packs is significant for a hybrid electric vehicle with providing data supports for the efficient and fine energy managements. In this brief, extended Kalman filter (EKF), multi-model extended Kalman filter (MMEKF) and adaptive fading extended Kalman filter (AFEKF) are used to estimate SOC respectively, then they are combined with a switching strategy to match their own features with that of SOC in different working areas. The estimation error of SOC is below 2.5 percent. And the strategy is verified to have good initialization stabilities and convergence behaviors.",

keywords = "Extended Kalman filter, Hybrid electric bus, Power battery pack, State of charge",

author = "Shanshan Xie and Rui Xiong and Yongzhi Zhang and Hongwen He",

note = "Publisher Copyright: {\textcopyright} 2017 The Authors. Published by Elsevier Ltd.; 8th International Conference on Applied Energy, ICAE 2016 ; Conference date: 08-10-2016 Through 11-10-2016",

year = "2017",

doi = "10.1016/j.egypro.2017.03.774",

language = "English",

volume = "105",

pages = "2678--2683",

journal = "Energy Procedia",

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TY - JOUR

T1 - The Estimation of State of Charge for Power Battery Packs used in Hybrid Electric Vehicle

AU - Xie, Shanshan

AU - Xiong, Rui

AU - Zhang, Yongzhi

AU - He, Hongwen

PY - 2017

Y1 - 2017

N2 - The estimation of state of charge (SOC) for power battery packs is significant for a hybrid electric vehicle with providing data supports for the efficient and fine energy managements. In this brief, extended Kalman filter (EKF), multi-model extended Kalman filter (MMEKF) and adaptive fading extended Kalman filter (AFEKF) are used to estimate SOC respectively, then they are combined with a switching strategy to match their own features with that of SOC in different working areas. The estimation error of SOC is below 2.5 percent. And the strategy is verified to have good initialization stabilities and convergence behaviors.

AB - The estimation of state of charge (SOC) for power battery packs is significant for a hybrid electric vehicle with providing data supports for the efficient and fine energy managements. In this brief, extended Kalman filter (EKF), multi-model extended Kalman filter (MMEKF) and adaptive fading extended Kalman filter (AFEKF) are used to estimate SOC respectively, then they are combined with a switching strategy to match their own features with that of SOC in different working areas. The estimation error of SOC is below 2.5 percent. And the strategy is verified to have good initialization stabilities and convergence behaviors.

KW - Extended Kalman filter

KW - Hybrid electric bus

KW - Power battery pack

KW - State of charge

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U2 - 10.1016/j.egypro.2017.03.774

DO - 10.1016/j.egypro.2017.03.774

M3 - Conference article

AN - SCOPUS:85020716753

SN - 1876-6102

VL - 105

SP - 2678

EP - 2683

JO - Energy Procedia

JF - Energy Procedia

T2 - 8th International Conference on Applied Energy, ICAE 2016

Y2 - 8 October 2016 through 11 October 2016

ER -

The Estimation of State of Charge for Power Battery Packs used in Hybrid Electric Vehicle

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Keywords

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