State-of-Charge and State-of-Health Estimation for Lithium-Ion Batteries Based on Dual Fractional-Order Extended Kalman Filter and Online Parameter Identification

Liuyi Ling,Ying Wei

doi:10.1109/access.2021.3068813

Liuyi Ling, Ying Wei

Open Access

https://doi.org/10.1109/access.2021.3068813

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Abstract

Accurate state-of-charge (SOC) and state-of-health (SOH) estimations of batteries are of great significance for electric vehicles. A combined SOC and SOH estimation method for lithium-ion batteries based on a dual extended Kalman filter (EKF) and fractional-order model (FOM) is proposed. A fractional second-order RC model is established and model parameters are identified offline by an adaptive genetic algorithm (AGA). One of the dual filters is used to jointly estimate the SOC and SOH (ohmic internal resistance and capacity), and another is employed to update the model parameters online. Compared with single filter with fixed parameters, the dual filters can obtain more accurate SOC estimation and model voltage prediction. The SOC root-mean square errors (RMSEs) decrease from 6.87%, 8.50% and 7.32% to 0.48%, 0.63% and 0.86% under the Federal Urban Driving Schedule (FUDS), the Dynamic Stress Test (DST) and the US06 Highway Driving Schedule tests, respectively, and the model voltage RMSEs decrease from 88.6 mV, 79.3 mV and 68.4 mV to 4.9 mV, 5.7 mV and 3.8 mV, respectively at room temperature. The accuracy of the SOH estimation is also verified under these three tests. The convergence and robustness of the proposed method are discussed and verified by using the wrong initial state value and noise analysis.

Highlights

Owing to the merits of high energy density, long cycle life and no memory effect, lithium-ion batteries have been widely used in electric vehicles (EV)
The main contributions of this work include: 1) We use fractional calculus to model the battery and all the model parameters are identified online, which can ensure the accuracy of the modeling no matter what working condition the battery is operated in; 2) We give the strict calculation process of dual fractional-order extended Kalman filter algorithm for joint estimation of state of charge (SOC) and state of health (SOH); 3) The accuracy of the proposed method is verified under the Federal Urban Driving Schedule (FUDS), the Dynamic Stress Test (DST) and the US06 Highway Driving Schedule at different temperatures
In order to verify the accuracy of the proposed algorithm, the battery SOC and SOH estimation are conducted under the FUDS, DST and US06 tests, respectively

Summary

INTRODUCTION

Owing to the merits of high energy density, long cycle life and no memory effect, lithium-ion batteries have been widely used in electric vehicles (EV). The dual estimation scheme is designed to estimate the SOC as well as SOH (ohmic internal resistance and capacity) and synchronously update each parameter of the fractional-order model online. The main contributions of this work include: 1) We use fractional calculus to model the battery and all the model parameters are identified online, which can ensure the accuracy of the modeling no matter what working condition the battery is operated in; 2) We give the strict calculation process of dual fractional-order extended Kalman filter algorithm for joint estimation of SOC and SOH; 3) The accuracy of the proposed method is verified under the Federal Urban Driving Schedule (FUDS), the Dynamic Stress Test (DST) and the US06 Highway Driving Schedule at different temperatures.

THE SECOND FOEKF FOR ONLINE PARAMETER IDENTIFICATION

ESTABLISHMENT OF THE OCV-SOC RELATIONSHIP

RESULTS AND DISCUSSION

CONCLUSION