Ageing Estimation of Lithium-Ion Batteries Applied to a Three-Wheel PHEV Roadster

Nadeau, Jonathan; Dubois, Maxime R.; Desrochers, Alain; Denis, Nicolas

doi:10.1109/vppc.2013.6671663

Cited by 9 publications

(8 citation statements)

References 5 publications

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“…Because of the changes occur inside the battery due to ageing, its performance and consequently its model alters [7]. For example it is demonstrated in [74] that how the cycling can lead to battery internal resistance growth and capacity loss using Thevenin battery model. So, an ageing factor is also essential in a perfect battery model.…”

Section: Ec Battery Model Variablesmentioning

confidence: 99%

A review on electric vehicle battery modelling: From Lithium-ion toward Lithium–Sulphur

Fotouhi

Auger

Propp

et al. 2016

Renewable and Sustainable Energy Reviews

657

320

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Accurate prediction of range of an electric vehicle (EV) is a significant issue and a key market qualifier. EV range forecasting can be made practicable through the application of advanced modelling and estimation techniques. Battery modelling and state-of-charge estimation methods play a vital role in this area. In addition, battery modelling is essential for safe charging/discharging and optimal usage of batteries. Much existing work has been carried out on incumbent Lithium-ion (Li-ion) technologies, but these are reaching their theoretical limits and modern research is also exploring promising next-generation technologies such as Lithium-Sulfur (Li-S). This study reviews and discusses various battery modelling approaches including mathematical models, electrochemical models and electrical equivalent circuit models. After a general survey, the study explores the specific application of battery models in EV battery management systems, where models may have low fidelity to be fast enough to run in real-time applications. Two main categories are considered: reducedorder electrochemical models and equivalent circuit models. The particular challenges associated with Li-S batteries are explored, and it is concluded that the state-of-the-art in battery modelling is not sufficient for this chemistry, and new modelling approaches are needed.

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Section: Ec Battery Model Variablesmentioning

confidence: 99%

A review on electric vehicle battery modelling: From Lithium-ion toward Lithium–Sulphur

Fotouhi

Auger

Propp

et al. 2016

Renewable and Sustainable Energy Reviews

657

320

View full text Add to dashboard Cite

show abstract

“…Most of these studies were carried out considering various cycle ageing conditions [12], [21] - [26] or different driving profiles [27], [28]. Furthermore, the aforementioned cycle ageing studies have been performed for different Li-ion battery chemistries, as follows.…”

Section: State-of-the-artmentioning

confidence: 99%

Degradation Behavior of Lithium-Ion Batteries During Calendar Ageing—The Case of the Internal Resistance Increase

Stroe

Świerczyński

Kar

et al. 2018

IEEE Trans. on Ind. Applicat.

106

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Lithium-ion batteries are regarded as the key energy storage technology for both e-mobility and stationary renewable energy storage applications. Nevertheless, Lithium-ion batteries are complex energy storage devices, which are characterized by a complex degradation behavior, which affects both their capacity and internal resistance. This paper investigates, based on extended laboratory calendar ageing tests, the degradation of the internal resistance of a Lithium-ion battery. The dependence of the internal resistance increase on the temperature and state-of-charge level have been extensive studied and quantified. Based on the obtained laboratory results, an accurate semi-empirical lifetime model, which is able to predict with high accuracy the internal resistance increase of the Lithium-ion battery over a wide temperature range and for all state-of-charge levels was proposed and validated.

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“…These two device where electronically controlled to produce current patterns which would occur in a real driving cycle. Those current curves come from a World-wide Motorcycle Emissions Test Cycle (WTMC) driving cycle applied to a three-wheel recreational motorcycle [13], [15], [16].…”

Section: Test Methodologymentioning

confidence: 99%

“…the Rin). Thus, the state of health (SOH) of a cell or of a battery pack will reflect a percentage of those performances by comparison with a brand new cell [13]. Firstly, a capacity fade threshold can be established for minimum drivable range for the electric vehicle.…”

Section: Soh Estimationmentioning

confidence: 99%

Real-Time Estimator Li-ion Cells Internal Resistance for Electric Vehicle Application

Tessier

Dubois

Trovão

2016

WEVJ

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Battery Management Systems (BMS) were introduced into Li-ion battery packs to serve several purposes, among which assertion of the small variations of the cells state of charge and increase of their life cycle. This paper offers a complete real-time working model for the on-line measurement of the cells internal resistance, which requires very low computation time and enough precision for electric vehicle applications. Some electric models used for the determination electrical parameters have a huge complexity and are not implementable in automotive processing units. Among many solutions, the proposed approach uses cell modeled as a source-resistance with low complexity, without loss measurement precision, or increase the computation capabilities.

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Ageing Estimation of Lithium-Ion Batteries Applied to a Three-Wheel PHEV Roadster

Cited by 9 publications

References 5 publications

A review on electric vehicle battery modelling: From Lithium-ion toward Lithium–Sulphur

A review on electric vehicle battery modelling: From Lithium-ion toward Lithium–Sulphur

Degradation Behavior of Lithium-Ion Batteries During Calendar Ageing—The Case of the Internal Resistance Increase

Real-Time Estimator Li-ion Cells Internal Resistance for Electric Vehicle Application

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