Li-ion Battery Modeling and State of Charge Estimation Method Including the Hysteresis Effect

Antonucci Antonucci,Panzavecchia Panzavecchia,Brunaccini Brunaccini,Tinè Tinè,Ferraro Ferraro,Cara Cara,Artale Artale,Caravello Caravello,Sergi Sergi,Cataliotti Cataliotti,Cosentino Cosentino,Guaiana Guaiana

doi:10.3390/electronics8111324

Abstract

In this paper, a new approach to modeling the hysteresis phenomenon of the open circuit voltage (OCV) of lithium-ion batteries and estimating the battery state of charge (SoC) is presented. A characterization procedure is proposed to identify the battery model parameters, in particular, those related to the hysteresis phenomenon and the transition between charging and discharging conditions. A linearization method is used to obtain a suitable trade-off between the model accuracy and a low computational cost, in order to allow the implementation of SoC estimation on common hardware platforms. The proposed characterization procedure and the model effectiveness for SoC estimation are experimentally verified using a real grid-connected storage system. A mixed algorithm is adopted for SoC estimation, which takes into account both the traditional Coulomb counting method and the developed model. The experimental comparison with the traditional approach and the obtained results show the feasibility of the proposed approach for accurate SoC estimation, even in the presence of low-accuracy measurement transducers.

Highlights

The estimation of battery state of charge (SoC) has been a topic of high interest in recent literature because it allows the available energy in the batteries and that which can still be stored to be identified.it is the main indicator of the system state and its knowledge allows the system security level to be increased and the success rate of optimization algorithms oriented to the maximum performance exploitation to be improved [1,2,3].SoC estimation methods are classified in [4] as direct methods [5,6,7]) and indirect methods
This paper aims to model the hysteresis phenomenon in a simple way, by linearizing the transition phase between the two open circuit voltages (OCVs)
It should be underlined that the battery characterization procedure and the experimental battery model parameters, into account both thethe hysteresis phenomenon and the consequent were performed at a fixed taking temperature

Summary

Introduction

SoC estimation methods are classified in [4] as direct methods (such as open circuit voltage estimation, Coulomb counting, and electrochemical impedance methods) [5,6,7]) and indirect methods (such as those based on artificial intelligence, adaptive filters, and models [8,9,10,11]). The main problems are related to the estimation algorithm’s computational cost and not zero-mean error noises, due, for example, to measurement sensors drifting or non-correct modeling of the hysteresis phenomenon. The latter aspect has not been well-investigated in the literature, even though this

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