Battery State-Of-Charge Estimation Based on a Dual Unscented Kalman Filter and Fractional Variable-Order Model

Abstract

State-of-charge (SOC) estimation is essential for the safe and effective utilization of lithium-ion batteries. As the SOC cannot be directly measured by sensors, an accurate battery model and a corresponding estimation method is needed. Compared with electrochemical models, the equivalent circuit models are widely used due to their simplicity and feasibility. However, such integer order-based models are not sufficient to simulate the key behavior of the battery, and therefore, their accuracy is limited. In this paper, a new model with fractional order elements is presented. The fractional order values are adaptively updated over time. For battery SOC estimation, an unscented fractional Kalman filter (UFKF) is employed based on the proposed model. Furthermore, a dual estimation scheme is designed to estimate the variable orders simultaneously. The accuracy of the proposed model is verified under different dynamic profiles, and the experimental results indicate the stability and accuracy of the estimation method.

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

“…Tian et al [96] used the UKF with a modified ECM to study the effects different temperatures and charge rates. Some variants of the UKF have also been reported [100][101][102][103][104][105][106][107]. In [100,102], an adaptive UKF (AUKF) adaptively adjusts the perturbation covariance of the state's value; the zero-state battery hysteresis model was selected to reduce the complexity [100].…”

“…The comparison showed that the AUKF has a better accuracy and convergence rate than the EKF, AEKF, and UKF. Cai et al [103] addressed the issue of the battery model accuracy. They proposed a fractional variable order model, which updates the value of the battery model adaptively.…”

“…Table 8 lists the performance of the UKF and its variants. ≤± 0.028% Cai et al 2017 [103] ME ≤ ± 1.51% Liu et al 2017 [104] ≤± 0.5% Peng et al 2017 [105] ≤± 1.49% Chen et al 2017 [106] ≤± 2.88% Li et al 2018 [107] ≤± 1.5% Li et al 2018 [108] ≤± 0.31%…”

Research on Parameter Self-Learning Unscented Kalman Filtering Algorithm and Its Application in Battery Charge of State Estimation

“…The UKF was implemented in several studies for the SoC estimation of the batteries. As in References [5,6], an adaptive unscented Kalman filter (AUKF) is proposed for SoC estimation, which reduces the complexity by adaptively adjusting the covariance of state values and selecting a zero-state battery hysteresis model. However, from the implementation of the UKF algorithm, we can find that the position of the Sigma points directly affects the accuracy of the state estimation by the UKF algorithm.…”

SOC Estimation with an Adaptive Unscented Kalman Filter Based on Model Parameter Optimization

“…In the traditional UKF algorithm [15][16][17][18][19], the covariance is a constant and cannot satisfy the real-time dynamic characteristics of the noise, which has a certain impact on the accuracy [20,21]. To eliminate this effect, the traditional UKF algorithm is improved by updating the covariance in real-time, which thus improves the accuracy of the UKF in this paper.…”