Abstract
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.
Highlights
With the problems due to industrial development and environmental pollution, electric vehicles are becoming more popular as an environmentally-friendly mode of transportation
We present the cubature particle filter (CPF) method for state of charge (SOC) estimation
The CPF algorithm was proposed for the SOC estimation of lithium-ion batteries in electric vehicles
Summary
With the problems due to industrial development and environmental pollution, electric vehicles are becoming more popular as an environmentally-friendly mode of transportation. The Coulomb counting method is widely used for SOC estimation of electric vehicles because it is a simple algorithm This method uses the SOC formula to compute the current integration and obtain the charged or discharged capacity. The CKF algorithm directly calculates the mean and variance of the state using the numerical integration method based on the cubature principle, and generates the importance density function of the PF algorithm using the mean and the variance. This approach combines aspects of the CKF algorithm and the PF algorithm. Equivalent circuit model and the model parameters are presented in Section 2; Section 3 introduces the CKF algorithm and the CPF algorithm in detail; Section 4 describes the schematic of the battery test; And in Section 5, the verification results and comparisons of the CKF algorithm and CPF algorithm for accuracy and robustness are presented, and as well as the summary and conclusions
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