A State of Charge Estimation Method Based on Adaptive Extended Kalman-Particle Filtering for Lithium-ion Batteries

Bizhong Xia, Shengkun Guo, Wei Wang, Yongzhi Lai, Huawen Wang, Mingwang Wang and Weiwei Zheng
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Bizhong Xia: Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
Shengkun Guo: Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
Wei Wang: Sunwoda Electronic Co. Ltd., Shenzhen 518108, China
Yongzhi Lai: Sunwoda Electronic Co. Ltd., Shenzhen 518108, China
Huawen Wang: Sunwoda Electronic Co. Ltd., Shenzhen 518108, China
Mingwang Wang: Sunwoda Electronic Co. Ltd., Shenzhen 518108, China
Weiwei Zheng: Sunwoda Electronic Co. Ltd., Shenzhen 518108, China

Energies, 2018, vol. 11, issue 10, 1-15

Abstract: A state of charge ( SOC ) estimation method is proposed. An Adaptive Extended Kalman Particle filter (AEKPF) based on Particle Filter (PF) and Adaptive Kalman Filter (AKF) is used in order to decrease the error and reduce calculations. The second-order resistor-capacitor (RC) Equivalent Circuit Model (ECM) is used to identify dynamic parameters of the battery. After testing (include Dynamic Stress test (DST), New European Driving Cycle (NEDC), Federal Urban Dynamic Schedule (FUDS), Urban Dynamometer Driving Schedules (UDDS), etc.) at different temperatures and times, it was found that the AEKPF exhibits greater tolerance for high system noise (10% or higher) and provides more accurate estimations under common operating conditions.

Keywords: lithium-ion battery; adaptive extended Kalman particle filter; second-order model; state of charge estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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