A review on electric vehicle battery modelling: From Lithium-ion toward Lithium–Sulphur
Introduction
Hybrid vehicles are well-established in the market, and electric vehicles are growing in popularity. This trend is likely to continue for the foreseeable future. There is a strong scientific consensus in the reality of human-made climate change [1], [2], which is reflected in national and international legislation on point-of-use emissions: in Europe, we are already seeing the introduction of stringent regulations. The UK Government has estimated that by 2030, average ‘new car’ tailpipe emissions will need to fall to around 50–70 g/km – a rough halving from the present day [3]. In discussions with our international academic colleagues, it is clear that in the relatively new, rapidly expanding markets such of China and India, there is a strong consciousness of the need to develop sustainably and without over-dependence on scarce foreign oil imports. There have been many studies that have considered the use of renewable energy sources in next generation of transport systems, and various new technologies have been applied [4], [5], [6]. The powertrain of the future is likely to be increasingly hybridised, increasingly electrified, and increasingly dependent on high quality, effective and affordable traction batteries.
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.
Development of energy storage systems is at the heart of vehicle electrification process. Many new technologies for batteries, fuel cells, ultracapacitors, etc. have been developed for implementation in hybrid and electric vehicles. A good example is the Lithium-ion (Li-ion) battery, one of the most widely used technologies in advanced electrified vehicles. Li-ion batteries have been developed to meet different specifications, each with different chemical compositions. Key design objectives for automotive applications include battery energy density, safety and reliability [9]. Among the different types of Li-ion batteries used in EVs are Lithium Cobalt Oxide (LCO), Lithium Manganese Oxide (LMO), Lithium Iron Phosphate (LFP) and Lithium Nickel–Manganese–Cobalt Oxide (NMC) [10]. Table 1 shows some of the battery pack manufacturers and the EVs in which their batteries are used [11].
As Li-ion batteries have been developed to maturity, they have begun to approach their theoretical energy density limits (200–250 W h/kg [12]). Ongoing electrochemical research on Li-ion batteries aims at increasing cycle life, safety, and other performance characteristics [13]. At the same time, researchers are investigating other types of electrochemical energy storage systems with higher energy density for use in EV applications. One such electrochemical system is the Lithium–Sulphur (Li–S) battery. The Li–S battery offers potential advantages over Li-ion, such as higher energy density, improved safety, a wider operating temperature range, and lower cost (because of the availability of Sulphur); this makes it a promising technology for EV application. However, Li–S technology has not been widely commercialised yet because it suffers from limitations such as self-discharge and capacity fades due to cycling and high discharge current [14]; research into these areas is ongoing.
Battery modelling is a significant task within battery technology development, and is vital in applications. For example, EV range prediction is only possible through the application of advanced battery modelling and estimation techniques to determine current state and predict remaining endurance. In addition, battery modelling is essential for safe charging and discharging, optimal utilisation of batteries, fast charging, and other applications. In this study, modelling of batteries is addressed with a focus on their EV applications. Different modelling approaches are reviewed and explained, considering three categories of models: mathematical models, electrochemical models and electrical equivalent circuit networks. The first part of the paper considers these techniques in general, and is potentially useful to a wide range of readers who are interested in understanding the breadth of techniques available for battery modelling, with many different possible applications. The paper then considers our specific application: hybrid and electric vehicles. This considers modelling approaches which are applicable in EV battery management systems: the discussions presented in this part are mainly focused on low-fidelity models which are fast enough for real-time applications. For this purpose, our review focuses on reduced-order (simplified) electrochemical models, and equivalent circuit network models. The last part of this study specifically considers Li–S battery technology which some researchers view as promising technology for the next generation of hybrid and electric vehicles. Previous studies about Li–S battery modelling are reviewed separately and the challenges of Li–S battery modelling for EV application are discussed.
Section snippets
Battery modelling approaches
There are many studies focused on battery modelling in the literature. Models in can be classified according to the different modelling approaches used. The major categories are mathematical models, electrochemical models and electrical equivalent circuit networks [15], [16]. The literature also contains examples of combined model types such as analytical–electrochemical models [17], [18]. In addition, battery thermal models have been investigated in a number of studies [19], [20], [21]. Pure
Battery modelling for EV application
Accurate prediction of range of an EV is a critical issue and a key market qualifier. EV range forecasting relies on the application of suitable modelling techniques. There are a variety of techniques, typically operating at different levels of fidelity and employing different modelling philosophies [46]. The battery model, as a part of the whole vehicle model, plays a significant role in the EV range calculation. Estimation of the EV range without the knowledge of accurate battery SOC is
Lithium–Sulphur battery: properties, modelling and challenges
The reason that a separate part of this article is allocated to Lithium–Sulphur (Li–S) battery, is the importance of this topic to the automotive industry in the near future. Indeed Li–S batteries with higher energy density, increased safety, wider temperature range of operation and lower cost because of the availability of Sulphur, is a promising technology for EV application. Considering just the first advantage, that is the higher energy density (theoretical capacity of 1675 mA h/g [12]), it
Conclusions
This paper has reviewed techniques for modelling batteries, with a particular focus on three families of techniques: mathematical models, electrochemical models, and equivalent electrical circuit network models. High-fidelity electrochemical models have the potential to offer extreme accuracy and insight, but they are not suitable for most real-time embedded applications. For battery management and range prediction in electric vehicles, there are two families of models that can be used. The
Acknowledgements
This research was undertaken as part of three projects: Revolutionary Electronic Battery (REVB, TS/L000903/1), co-funded by Innovate UK; the Future Vehicle Project (EP/I038586/1) funded by EPSRC, and Cranfield University׳s Impact Acceleration Account (EP/K503927/1), also funded by EPSRC.
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