Impact of Battery Ageing on an Electric Vehicle Powertrain Optimisation

Auger, Daniel J.; Groff, Maxime F.; Mohan, Ganesh; Longo, Stefano; Assadian, Francis

doi:10.13044/j.sdewes.2014.02.0028

Cited by 9 publications

(9 citation statements)

References 11 publications

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“…Although having such a model might be enough in some cases, there is still another issue which is not considered, battery ageing or cycling degradation. 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.…”

Section: Ec Battery Model Variablesmentioning

confidence: 99%

“…In the UK, we have some uptake of electric vehicles, but EVs still represent a small market sector and there are challenges associated with their introduction [7]. Although it has been shown that in their present form, electric vehicles are suitable for the day-to-day needs of the typical urban motorist [8], consumers still have concerns about cost, longevity and range [7]. Charging times and safety are also well-known concerns.…”

Section: Introductionmentioning

confidence: 99%

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

Fotouhi

Auger

Propp

et al. 2016

Renewable and Sustainable Energy Reviews

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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%

Section: Introductionmentioning

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

Self Cite

657

320

View full text Add to dashboard Cite

show abstract

“…electric vehicle and this should be enhanced in promotional policies because it is a highly important customer preference. Even »green oriented« consumers take lower carbon emissions as a bonus rather that the main objective [53]. Even battery as the most expensive component of electric vehicle and its aging affecting battery capacity can significantly affect electric vehicle value and can be relevant factor for decision on buying used electric vehicle [54].…”

Section: Customer Preferences and Opinions About Levmentioning

confidence: 99%

Policies for Promotion of Electric Vehicles and Factors Influencing Consumers’ Purchasing Decisions of Low Emission Vehicles

Knez¹,

Obrecht²

2017

J. sustain. dev. energy water environ. syst.

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Recently different studies of green transport have become interesting for policy makers, car manufacturers, customers and energy suppliers. Many stakeholders from the public and private sectors are investing a lot of effort to identify consumer behaviour for future improvements in development of green products and effective strategies, which could accelerate the transition to sustainable future. This paper presents the effects of electric vehicle promotional policies and customer preferences about alternative fuel vehicles. This study has shown that the electric vehicle promotional policies adopted in Slovenia have been unsuccessful, as the share of first-time registered electric vehicles in 2013 was below 1%. For different segments of people whose opinions about low emission vehicles differ, different measures must be adopted. When designing promotional policies focus must be on the most relevant factors such as the total vehicle price and fuel economy.

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“…Some battery models are available, e.g. [20], [21]. Similar models are needed for other components such as electric machines and fuel cells.…”

Section: Developing Vehicle Modelsmentioning

confidence: 99%

Mechatronics in Sustainable Mobility: Two Electric Vehicle Applications

Longo¹,

Auger²,

Assadian³

2014

j sustain mobility

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In this paper, we first review the role that mecha tronics and advanced control have in modern road vehicles, in particular their present and potential impact on sustainable mobility. We then illustrate this with two research examples. Firstly, we show how electronic science, control system tech niques and computing manifest themselves in the design of an advanced battery management algorithm designed to estimate two unmeasurable but vital quantities, State of Charge (SoC) and State of Health (SoH): this allows better utilisation of battery capacity, with scope for advanced prognostics and diagnostics. Secondly, we show how multi-domain modelling integrating mechanical science and electronic science can be used to express component ageing as part of a set of vehicle-level performance objectives and used to explore the trade-offs between conflicting requirements, aiding sensible design choices. O Automotive Engineering O Mechatronics O Electric Vehicles O Battery Estimation and Health O Electric Vehicle Component Ageing Stefano Longo (MSc, PhD, MIEEE, MIET) received his MSc in Control Systems from the University of Sheffield in 2007 and completed his PhD in Control Systems at the University of Bristol in 2011. His PhD thesis was awarded the Institution of Engineering and Technology (IET) Control and Automation Prize for significant achievements in the area of control engineering. In November 2010 he was appointed to the position of Research Associate at Imperial College London. He was appointed to the position of lecturer in Vehicles' Electrical and Electronic Systems at Cranfield University in the summer of 2012. Daniel Auger is an academic at Cranfield University, where he teaches automotive mechatronics and researches automotive energy storage, optimisation and advanced control. After studying engineering at Cambridge, Daniel joined AMS (formerly Marconi Radar, now BAE Systems) where he worked as a senior engineer and design authority on the Seawolf Mid-Life Update (SWMLU) programme. Following this, he joined MathWorks as a senior consultant, managing and working on a variety of projects. Dr Auger joined Cranfield at the start of 2013 as a lecturer in advanced control & optimisation in the School of Engineering's Centre for Automotive Engineering.

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Impact of Battery Ageing on an Electric Vehicle Powertrain Optimisation

Cited by 9 publications

References 11 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

Policies for Promotion of Electric Vehicles and Factors Influencing Consumers’ Purchasing Decisions of Low Emission Vehicles

Mechatronics in Sustainable Mobility: Two Electric Vehicle Applications

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