Sensitivity analysis of lithium-ion battery model to battery parameters

Rahimi-Eichi, Habiballah; Balagopal, Bharat; Chow, Mo-Yuen; Yeo, Taejung

doi:10.1109/iecon.2013.6700257

Cited by 9 publications

(7 citation statements)

References 17 publications

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“…To accurately estimate the battery SOC, a battery model may be improved and corrected by adding more parameters [2,7,[19][20]. Adding more parameters increases the complexity of the model.…”

Section: Test Vehicle Field Data and Proposed Methodsmentioning

confidence: 99%

SOC Estimation for Traction Battery Pack of Electric Vehicles with Battery Degradation Tracking

Saikong¹,

Sumpavakup²,

Kulworawanichpong³

2019

IJIES

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In the electric vehicle industry, a good estimation of a traction battery pack or the state of charge (SOC) is crucial as it reflects how far a vehicle travel before recharging. As the battery degrades, its behavior and the associated parameters such as internal resistance, capacity and SOC-OCV (open circuit voltage) mapping changes. Thus, a battery model has to take into account the changes in the battery parameters for it to be accurate throughout the battery lifetime. For such a model to be computational intensive, it requires powerful processors. With limited calculation performance processors found in vehicles, the model fidelity is normally compromised. In this paper, two battery models are used to accurately estimate traction battery SOC; The Ohmic resistance model is used to sense changes in battery internal resistance, when the change is significant, the resistor-capacitor (RC) model is used to update the battery SOC-OCV curve which is used to estimate the battery initial SOC. Hence, the coulomb counting method is used to update the battery SOC. The real operational battery data from PEA Ze-Bus (Zero-Emission bus of the Provincial Electricity Authority of Thailand) are used in this study. The proposed algorithm used to test the state of charge of the battery has been verified and illustrates the error of SOC estimation at 3.31%, less than the unadaptable model.

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Section: Test Vehicle Field Data and Proposed Methodsmentioning

confidence: 99%

SOC Estimation for Traction Battery Pack of Electric Vehicles with Battery Degradation Tracking

Saikong¹,

Sumpavakup²,

Kulworawanichpong³

2019

IJIES

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“…A full literature review of LIB SA is available in [23], which is briefly outlined in this section. Schmidt et al, 2010 [24], Rahimi-Eichi et al 2013 [25], and Pózna et al 2017 [26] have used local methods to perform SA on LIB ECM. Whilst ECM have a linear model structure, they are non-linear in the parameters (functions of SOC and temperature).…”

Section: Literature Reviewmentioning

confidence: 99%

Global Sensitivity Analysis of the Single Particle Lithium-Ion Battery Model with Electrolyte

Grandjean

Odio

et al. 2019

2019 IEEE Vehicle Power and Propulsion Conference (VPPC)

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“…To enhance the model accuracy, different sensitivity analyses have been proposed in the literature. In particular, the effects of changes in the model parameters on battery terminal voltage are analysed in [14]. Furthermore, a global sensitivity analysis of ECM parameters is studied in [8], reporting the strong dependence parameter on the SoC.…”

Section: Introductionmentioning

confidence: 99%

Improvement of an Equivalent Circuit Model for Li-Ion Batteries Operating at Variable Discharge Conditions

et al. 2020

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A real time simulation of battery conditions is an essential step in the development of energy harvesting devices. Since it is not possible to have a direct measurement, the battery information, such as the remaining charge, need to be estimated by means of model-based estimation algorithms. Most of the existing models describing battery behaviour, are suitable only for a constant discharge current. This paper proposes a study of the dependence of the equivalent circuit model parameters on different discharge conditions. The model presented provides a powerful tool to represent the batteries’ behaviour in energy harvesting systems, involving continuous charge and discharge cycles. The extraction of parameters was performed, starting from a set of reference curves generated in Matlab Simulink environment, referring to Li-ion technology batteries. The parameters were extracted by means of a cascade of global and local search identification algorithms. Finally, the relations describing parameters’ behaviours as functions of the discharge current are presented.

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Sensitivity analysis of lithium-ion battery model to battery parameters

Cited by 9 publications

References 17 publications

SOC Estimation for Traction Battery Pack of Electric Vehicles with Battery Degradation Tracking

SOC Estimation for Traction Battery Pack of Electric Vehicles with Battery Degradation Tracking

Global Sensitivity Analysis of the Single Particle Lithium-Ion Battery Model with Electrolyte

Improvement of an Equivalent Circuit Model for Li-Ion Batteries Operating at Variable Discharge Conditions

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