Overview of Lithium-Ion Battery Modeling Methods for State-of-Charge Estimation in Electrical Vehicles

Jinhao Meng,Mattia Ricco,Daniel-Ioan Stroe,Remus Teodorescu,Guangzhao Luo,Maciej Swierczynski

doi:10.3390/app8050659

Jinhao Meng, Mattia Ricco + Show 4 more

Open Access

https://doi.org/10.3390/app8050659

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Abstract

As a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time.

Highlights

As fossil fuel reserves continue to decrease, new means of transportation that are independent of traditional fuels have to be found to meet the requirements of our daily life
By analyzing the structure of the model-based State of Charge (SOC) estimation, it is found that the performance of SOC estimation is closely related to model-based SOC estimation, it is found that the performance of SOC estimation is closely related to the accuracy of the battery model
This paper provides an extended review of the battery modeling methods

Summary

Introduction

As fossil fuel reserves continue to decrease, new means of transportation that are independent of traditional fuels have to be found to meet the requirements of our daily life. In order to increase the acceptance of EV in the market, the performance of the energy storage system has attracted much attention from both industry and academia. As the only power supply in pure EV, the capability of the battery pack is of great importance. Lithium-ion (Li-ion) batteries have higher energy density, longer cycle life, and no memory effect compared with other battery types [1,2]. In order to ensure the safety and reliable operation of the battery pack, SOC has to be estimated [4,5]

Methods

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Discussion

Conclusion