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

Energies

Shengkun Guo¹  Bizhong Xia¹  Weiwei Zheng²  Wei Wang²  Huawen Wang²  Yongzhi Lai²  Mingwang Wang² 

[1] Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China;Sunwoda Electronic Co. Ltd., Shenzhen 518108, China;

关键词: lithium-ion battery;    adaptive extended Kalman particle filter;    second-order model;    state of charge estimation;   

DOI  :  10.3390/en11102755

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown