期刊
ENERGIES
卷 11, 期 10, 页码 -出版社
MDPI
DOI: 10.3390/en11102755
关键词
lithium-ion battery; adaptive extended Kalman particle filter; second-order model; state of charge estimation
资金
- Economic, Trade and Information Commission Shenzhen of Shenzhen Municipality Strategic Emerging Industries and Future Industrial Development Innovation Chain + Industrial Chain Project
- National Natural Science Foundation of China [51877120]
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.
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