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^*

^*此作品的通讯作者

机械与车辆学院

Beijing Institute of Technology

科研成果: 期刊稿件 › 会议文章 › 同行评审

19 引用（Scopus）

摘要

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.

源语言	英语
页（从-至）	2678-2683
页数	6
期刊	Energy Procedia
卷	105
DOI	https://doi.org/10.1016/j.egypro.2017.03.774
出版状态	已出版 - 2017
活动	8th International Conference on Applied Energy, ICAE 2016 - Beijing, 中国期限: 8 10月 2016 → 11 10月 2016

访问文件

10.1016/j.egypro.2017.03.774

其它文件与链接

链接到 Scopus 的出版物

引用此

@article{c8989a6e6858404d91456b9a55e6f93d,

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",

issn = "1876-6102",

publisher = "Elsevier Ltd.",

}

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

UR - https://www.scopus.com/pages/publications/85020716753

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

摘要

访问文件

其它文件与链接

指纹

引用此