A multi‐timescale adaptive dual particle filter for state of charge estimation of lithium‐ion batteries considering temperature effect

Abstract

Abstract Accurate estimation of state of charge (SOC) of lithium‐ion battery is one of the most crucial issues of battery management system. Temperature has a strong impact on the SOC estimation and the parameters of battery model, such as capacity and open circuit voltage. To improve the accuracy and robustness of battery state estimation, this paper tries to make the following three contributions: (a) A second‐order RC battery model is established considering the recovery capacity finding from experimental data for achieving accurate battery dynamic behavior simulation against the dynamic load conditions, especially the current and temperature are both time‐varying condition. (b) A multi‐timescale adaptive dual particle filter is proposed to identify the battery parameters and estimate the battery SOC with online measured data for satisfying the fast‐varying behavior of SOC and slow‐varying behavior of battery parameters. The battery parameters are identified with macrotimescale, while the battery state is estimated with microtimescale. (c) The proposed method is validated through a set of experiments, including the time‐varying temperature condition, which is overlooked by most previous literatures. The experimental results show that the proposed approach can achieve accurate and robust SOC estimation on a wide range of temperatures.

Keywords

• adaptive particle filter
• lithium‐ion battery
• multi‐timescale
• state of charge
• temperature effect