Fading Kalman filter-based real-time state of charge estimation in LiFePO4 battery-powered electric vehicles
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- 作者：KaiChin Lima ; kcl502@uowmail.edu.au" class="auth_mail" title="E-mail the corresponding author ; Hany Ayad Bastawrousa ; b ; Hany.Bastawrous@bue.edu.eg" class="auth_mail" title="E-mail the corresponding author ; Van-Huan Duonga ; vhd931@uowmail.edu.au" class="auth_mail" title="E-mail the corresponding author ; Khay Wai Seea ; kwsee@uow.edu.au" class="auth_mail" title="E-mail the corresponding author ; Peng Zhanga ; pengz@uow.edu.au" class="auth_mail" title="E-mail the corresponding author ; Shi Xue Doua ; shi@uow.edu.au" class="auth_mail" title="E-mail the corresponding author
- 关键词：Fading Kalman filter ; Open circuit voltage ; Real-time estimation ; Recursive least squares ; State of charge
- 刊名：Applied Energy
- 出版年：2016
- 出版时间：1 May 2016
- 年：2016
- 卷：169
- 期：Complete
- 页码：40-48
- 全文大小：1494 K
文摘
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Real-time battery model parameters and SoC estimation with novel method is proposed.
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Cascading filtering stages are used for parameters identification and SoC estimation.
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Optimized fading Kalman filter is implemented for SoC estimation.
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Accurate SoC estimation is validated in UDDS load profile experiment.
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This approach is suitable for BMS in EV applications due to its simplicity.