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The state of charge (SOC) is the residual capacity of a battery. The SOC value indicates the mileage endurance, and an accurate SOC value is required to ensure the safe use of the battery to prevent over- and over-discharging. However, unlike size and weight, battery power is not easily determined. As a consequence, we can only estimate the SOC value based on the external characteristics of the battery. In this paper, a cubature particle filter (CPF) based on the cubature Kalman filter (CKF) and the particle filter (PF) is presented for accurate and reliable SOC estimation. The CPF algorithm combines the CKF and PF algorithms to generate a suggested density function for the PF algorithm based on the CKF. The second-order resistor-capacitor (RC) equivalent circuit model was used to approximate the dynamic performance of the battery, and the model parameters were identified by fitting. A dynamic stress test (DST) was used to separately estimate the accuracy and robustness of the CKF and the CPF algorithms. The experimental results show that the CPF algorithm exhibited better accuracy and robustness than the CKF algorithm. © 2017 by the authors. Licensee MDPI, Basel, Switzerland.

A Cubature Particle Filter Algorithm to Estimate the State of the Charge of Lithium-Ion Batteries Based on a Second-Order Equivalent Circuit Model