Journal Article

A Comparative Study of Three Improved Algorithms Based on Particle Filter Algorithms in SOC Estimation of Lithium Ion Batteries

Sun, Wei, Cui, Deyu, Wang, Mingwang, Zhang, Ruifeng, Lao, Zizhou, Wang, Wei, Lai, Yongzhi, Xia, Bizhong, Sun, Zhen

2017

Overview

The state of charge (SOC) is an important parameter for batteries, especially those for electric vehicles. Since SOC cannot be obtained directly by measurement, SOC estimation methods are required. In this paper, three model-based methods, including the extended particle filter (EPF), cubature particle filter (CPF), and unscented particle filter (UPF), are compared in terms of complexity, accuracy, and robustness. The second-order resistor-capacitor (RC) equivalent circuit model is selected as the circuit model of the lithium-ion battery, and the parameters of the model are obtained by off-line identification. Then, the City test is applied to compare the performance of the methods. The experimental results show that the EPF method exhibits low complexity and fast running speed, but poor accuracy and robustness. Compared with the EPF method, the complexity of the CPF and UPF methods is relatively high, but these models offer improved accuracy and robustness.

• Publisher: MDPI AG
• Subject: Accuracy / Algorithms / Circuits / cubature particle filter (CPF) / extended particle filter (EPF) / Lithium / lithium-ion battery / state of charge / unscented particle filter (UPF)