Fractional-order modeling of lithium-ion batteries using additive noise assisted modeling and correlative information criterion

Improved Parameter Identification for Lithium-Ion Batteries Based on Complex-Order Beetle Swarm Optimization Algorithm

With the aim of increasing the model accuracy of lithium-ion batteries (LIBs), this paper presents a complex-order beetle swarm optimization (CBSO) method, which employs complex-order (CO) operator concepts and mutation into the traditional beetle swarm optimization (BSO). Firstly, a fractional-order equivalent circuit model of LIBs is established based on electrochemical impedance spectroscopy (EIS). Secondly, the CBSO is used for model parameters' identification, and the model accuracy is verified by simulation experiments. The root-mean-square error (RMSE) and maximum absolute error (MAE) optimization metrics show that the model accuracy with CBSO is superior when compared with the fractional-order BSO.

State-of-Charge Estimation of Lithium-Ion Batteries Based on Fractional-Order Square-Root Unscented Kalman Filter

The accuracy of the state-of-charge (SOC) estimation of lithium batteries affects the battery life, driving performance, and the safety of electric vehicles. This paper presents a SOC estimation method based on the fractional-order square-root unscented Kalman filter (FSR-UKF). Firstly, a fractional second-order Resistor-Capacitance (RC) circuit model of the lithium battery is derived. The accuracy of the parameterized model is verified, revealing its superiority over integer-order standard descriptions. Then, the FSR-UKF algorithm is developed, combining the advantages of the square-root unscented Kalman filter and the fractional calculus. The effectiveness of the proposed algorithm is proven under a variety of operational conditions in the perspective of the root-mean-squared error, which is shown to be below 1.0%. In addition, several experiments illustrate the performance of the FSR-UKF.

Optimal charging of fractional-order circuits with Cuckoo search

INTRODUCTION

Optimal charging of RC circuits is a well-studied problem in the integer-order domain due to its importance from economic and system temperature hazards perspectives. However, the fractional-order counterpart of this problem requires investigation.

OBJECTIVES

This study aims to find approximate solutions of the most energy-efficient input charging function in fractional-order RC circuits.

METHODS

This paper uses a meta-heuristic optimization technique called Cuckoo search optimizer to attain the maximum charging efficiency of three common fractional-order RC circuits. An analytical expression of the fractional capacitor voltage is suggested such that it satisfies the boundary conditions of the optimal charging problem. The problem is formulated as a fractional-order calculus of variations problem with compositional functional. The numerical solutions are obtained with the meta-heuristic optimization algorithm's help to avoid the complexities of the analytical approach.

RESULTS

he efficiency surfaces and input voltage charging curves are discussed for fractional-order in the range 0.5 < α ≤ 1 .

CONCLUSION

The optimized charging function can approximate the optimal charging curve using at most 4 terms. The charging time and the resistive parameters have the most dominant effect on charging efficiency at constant fractional-order α .