Abstract

Battery models have gained great importance in recent years, thanks to the increasingly massive penetration of electric vehicles in the transport market. Accurate battery models are needed to evaluate battery performances and design an efficient battery management system. Different modeling approaches are available in literature, each one with its own advantages and disadvantages. In general, more complex models give accurate results, at the cost of higher computational efforts and time-consuming and costly laboratory testing for parametrization. For these reasons, for early stage evaluation and design of battery management systems, models with simple parameter identification procedures are the most appropriate and feasible solutions. In this article, three different battery modeling approaches are considered, and their parameters’ identification are described. Two of the chosen models require no laboratory tests for parametrization, and most of the information are derived from the manufacturer’s datasheet, while the last battery model requires some laboratory assessments. The models are then validated at steady state, comparing the simulation results with the datasheet discharge curves, and in transient operation, comparing the simulation results with experimental results. The three modeling and parametrization approaches are systematically applied to the LG 18650HG2 lithium-ion cell, and results are presented, compared and discussed.

Highlights

  • The strong development of electric vehicles (EVs) moves the research actions on new solutions for the automotive field

  • The models are validated at steady state, comparing the simulation results with the datasheet discharge curves, and in transient operation, comparing the simulation results with experimental results

  • This article carried out a comparison study of battery modeling and parameter identification techniques

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Summary

Introduction

The strong development of electric vehicles (EVs) moves the research actions on new solutions for the automotive field. Batteries and storage systems represent the burning issues of this sector. Lithium ion batteries represent the main technology that is catching on for different purposes. These kinds of batteries are used for both stationary and mobile applications because of their high-energy density and high-power density. Knowing SoC (State of Charge) [2,3], SoH (State of Health) [4], OCV (Open Circuit Voltage) [5], currents, and voltages is absolutely necessary in order to have well designed and efficient energy storage systems, because nonlinear physical effects in batteries strongly influence battery lifetime [6]

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