Hybrid State of Charge Estimation of Lithium-Ion Battery Using the Coulomb Counting Method and an Adaptive Unscented Kalman Filter

Author:

Fahmy Hend M.1,Swief Rania A.1,Hasanien Hany M.12ORCID,Alharbi Mohammed3ORCID,Maldonado José Luis4ORCID,Jurado Francisco4ORCID

Affiliation:

1. Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt

2. Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt

3. Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia

4. Department of Electrical Engineering, Superior Polytechnic School of Linares, University of Jaén, 23700 Linares, Spain

Abstract

This paper establishes an accurate and reliable study for estimating the lithium-ion battery’s State of Charge (SoC). An accurate state space model is used to determine the parameters of the battery’s nonlinear model. African Vultures Optimizers (AVOA) are used to solve the issue of identifying the battery parameters to accurately estimate SoC. A hybrid approach consists of the Coulomb Counting Method (CCM) with an Adaptive Unscented Kalman Filter (AUKF) to estimate the SoC of the battery. At different temperatures, four approaches are applied to the battery, varying between including load and battery fading or not. Numerical simulations are applied to a 2.6 Ahr Panasonic Li-ion battery to demonstrate the hybrid method’s effectiveness for the State of Charge estimate. In comparison to existing hybrid approaches, the suggested method is very accurate. Compared to other strategies, the proposed hybrid method achieves the least error of different methods.

Funder

King Saud University, Riyadh, Saudi Arabia

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

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