Joint Estimation Method with Multi-Innovation Unscented Kalman Filter Based on Fractional-Order Model for State of Charge and State of Health Estimation

Abstract

This study simulates the polarization effect during the process of battery charging and discharging, and investigates the characteristics of the process. A fractional-order model (FOM) is established and the parameters of the FOM are identified with the adaptive genetic algorithm. As Kalman filter estimation causes error accumulation over time, using the fractional-order multi-innovation unscented Kalman filter (FOMIUKF) is a better choice for state of charge (SOC) estimation. A comparative study shows that the FOMIUKF has higher accuracy. A multiple timescales-based joint estimation algorithm of SOC and state of health is established to improve SOC estimation precision and reduce the amount of computation. The FOMIUKF algorithm is used for SOC estimation, while the UKF algorithm is used for SOH estimation. The joint estimation algorithm is then compared and analyzed alongside other Kalman filter algorithms under different dynamic operating conditions. Experimental results show that the joint estimation algorithm possesses high estimation accuracy with a mean absolute error of under 1% and a root mean square error of 1.35%.

A Novel Nonlinear Filter-Based Robust Adaptive Control Method for a Class of Nonlinear Discrete-Time Systems

“…where γ(k) is a monotonic increasing function of p(k), and its values range from 1 to 4, i.e., γ(k) ∈ [1,4]. Thus, it is concluded that γ(k) is a non-decreasing, bounded, and positive tuning factor.…”

“…In past years, a substantial amount of research has been devoted to advancing the field of adaptive control for continuous-time nonlinear systems. Numerous industrial processes exhibit nonlinear behavior, and various methods have been developed for identifying nonlinear systems, which can be applied to input nonlinear systems, output nonlinear systems, and systems with both input and output nonlinearities [1][2][3][4].…”

Joint State of Charge (SOC) and State of Health (SOH) Estimation for Lithium-Ion Batteries Packs of Electric Vehicles Based on NSSR-LSTM Neural Network

“…In terms of estimating the SOC of EV battery pack, there are typically four common methods [12,13], which include ampere-hour (Ah) integral methods [14], OCV lookup SOC table methods [15], filter algorithms combined with equivalent circuit models [16], and data-driven algorithms [3]. The ampere-hour (Ah) integral method is commonly used to estimate SOC by integrating current and time over different time intervals.…”