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.
Highlights
Lithium-ion batteries have a greater energy density, lower costs, self-discharge rates, and longer life cycles, and the majority of recent studies of BMS (Battery Management System) have focused on this type of battery [1,2,3,4,5,6,7,8]
This paper proposes a new state of charge (SOC) estimation method based on adaptive extended Kalman particle filter (AEKPF)
Ut = U0 − U1 − U2 − R0 It where U1 and U2 represent the terminal voltage of capacitances C1 and C2, respectively; Ut is the terminal voltage of the battery; It is the current of the battery; and U0 is the open-circuit voltage (OCV), which has a non-linear function relationship with SOC at a particular temperature
Summary
Lithium-ion batteries have a greater energy density, lower costs, self-discharge rates, and longer life cycles, and the majority of recent studies of BMS (Battery Management System) have focused on this type of battery [1,2,3,4,5,6,7,8]. Energies 2018, 11, 2755 basis function neural networks This type of system has self-learning capabilities and does not need to be based on a model, requiring a data set of the battery schedule to train. Considering the Adaptive Kalman Filter (AKF) and Particle Filter (PF), these two filters can compensate for some of each other’s shortcomings, and the proposed method might solve issues related to PF accuracy and its dependence on the amount of calculations, as well as the dependence of the AKF on the initial and input values This method is a classic closed-loop control method, which means it could be improved with other closed-loop methods in later research. The results demonstrate that the AEKPF method performs well in terms of accuracy and robustness when compared to previous approaches in a similar environment as in this paper
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
