Physical properties of a new Deep Eutectic Solvent based on lithium bis[(trifluoromethyl)sulfonyl]imide and N-methylacetamide as superionic suitable electrolyte for lithium ion batteries and electric double layer capacitors

Herein we present a study on the physical/chemical properties of a new Deep Eutectic Solvent (DES) based on N-methylacetamide (MAc) and lithium bis[(trifluoromethyl)sulfonyl]imide (LiTFSI). Due to its interesting properties, such as wide liquid-phase range from -60°C to 280°C, low vapor pressure, and high ionic conductivity up to 28.4mScm at 150°C and at x=1/4, this solution can be practically used as electrolyte for electrochemical storage systems such as electric double-layer capacitors (EDLCs) and/or lithium ion batteries (LiBs). Firstly, relationships between its transport properties (conductivity and viscosity) as a function of composition and temperature were discussed through Arrhenius' Law and Vogel-Tamman-Fulcher (VTF) equations, as well as by using the Walden classification. From this investigation, it appears that this complex electrolyte possesses a number of excellent transport properties, like a superionic character for example. Based on which, we then evaluated its electrochemical performances as electrolyte for EDLCs and LiBs applications by using activated carbon (AC) and lithium iron phosphate (LiFePO) electrodes, respectively. These results demonstrate that this electrolyte has a good compatibility with both electrodes (AC and LiFePO) in each testing cell driven also by excellent electrochemical properties in specific capacitance, rate and cycling performances, indicating that the LiTFSI/MAc DES can be a promising electrolyte for EDLCs and LiBs applications especially for those requiring high safety and stability. © 2013 Elsevier Ltd.

A new self-learning TLBO algorithm for RBF neural modelling of batteries in electric vehicles

One of the main purposes of building a battery model is for monitoring and control during battery charging/discharging as well as for estimating key factors of batteries such as the state of charge for electric vehicles. However, the model based on the electrochemical reactions within the batteries is highly complex and difficult to compute using conventional approaches. Radial basis function (RBF) neural networks have been widely used to model complex systems for estimation and control purpose, while the optimization of both the linear and non-linear parameters in the RBF model remains a key issue. A recently proposed meta-heuristic algorithm named Teaching-Learning-Based Optimization (TLBO) is free of presetting algorithm parameters and performs well in non-linear optimization. In this paper, a novel self-learning TLBO based RBF model is proposed for modelling electric vehicle batteries using RBF neural networks. The modelling approach has been applied to two battery testing data sets and compared with some other RBF based battery models, the training and validation results confirm the efficacy of the proposed method.

Computational scheduling methods for integrating plug-in electric vehicles with power systems: a review

Traditional internal combustion engine vehicles are a major contributor to global greenhouse gas emissions and other air pollutants, such as particulate matter and nitrogen oxides. If the tail pipe point emissions could be managed centrally without reducing the commercial and personal user functionalities, then one of the most attractive solutions for achieving a significant reduction of emissions in the transport sector would be the mass deployment of electric vehicles. Though electric vehicle sales are still hindered by battery performance, cost and a few other technological bottlenecks, focused commercialisation and support from government policies are encouraging large scale electric vehicle adoptions. The mass proliferation of plug-in electric vehicles is likely to bring a significant additional electric load onto the grid creating a highly complex operational problem for power system operators. Electric vehicle batteries also have the ability to act as energy storage points on the distribution system. This double charge and storage impact of many uncontrollable small kW loads, as consumers will want maximum flexibility, on a distribution system which was originally not designed for such operations has the potential to be detrimental to grid balancing. Intelligent scheduling methods if established correctly could smoothly integrate electric vehicles onto the grid. Intelligent scheduling methods will help to avoid cycling of large combustion plants, using expensive fossil fuel peaking plant, match renewable generation to electric vehicle charging and not overload the distribution system causing a reduction in power quality. In this paper, a state-of-the-art review of scheduling methods to integrate plug-in electric vehicles are reviewed, examined and categorised based on their computational techniques. Thus, in addition to various existing approaches covering analytical scheduling, conventional optimisation methods (e.g. linear, non-linear mixed integer programming and dynamic programming), and game theory, meta-heuristic algorithms including genetic algorithm and particle swarm optimisation, are all comprehensively surveyed, offering a systematic reference for grid scheduling considering intelligent electric vehicle integration.

An integrated approach for real-time model-based state-of-charge estimation of lithium-ion batteries

Lithium-ion batteries have been widely adopted in electric vehicles (EVs), and accurate state of charge (SOC) estimation is of paramount importance for the EV battery management system. Though a number of methods have been proposed, the SOC estimation for Lithium-ion batteries, such as LiFePo4 battery, however, faces two key challenges: the flat open circuit voltage (OCV) vs SOC relationship for some SOC ranges and the hysteresis effect. To address these problems, an integrated approach for real-time model-based SOC estimation of Lithium-ion batteries is proposed in this paper. Firstly, an auto-regression model is adopted to reproduce the battery terminal behaviour, combined with a non-linear complementary model to capture the hysteresis effect. The model parameters, including linear parameters and non-linear parameters, are optimized off-line using a hybrid optimization method that combines a meta-heuristic method (i.e., the teaching learning based optimization method) and the least square method. Secondly, using the trained model, two real-time model-based SOC estimation methods are presented, one based on the real-time battery OCV regression model achieved through weighted recursive least square method, and the other based on the state estimation using the extended Kalman filter method (EKF). To tackle the problem caused by the flat OCV-vs-SOC segments when the OCV-based SOC estimation method is adopted, a method combining the coulombic counting and the OCV-based method is proposed. Finally, modelling results and SOC estimation results are presented and analysed using the data collected from LiFePo4 battery cell. The results confirmed the effectiveness of the proposed approach, in particular the joint-EKF method.

Battery modelling methods for electric vehicles - a review

Electric vehicles (EVs) and hybrid electric vehicles (HEVs) are rapidly gaining popularity as a means of de-carbonization in the transport sector in tackling sustainable energy supply and environment pollution problems. To build a proper battery model is essential in predicting battery behaviour under various operating conditions for avoiding unsafe battery operations and developing proper controlling algorithms and maintenance strategies. This paper presents a comprehensive review of battery modelling methods. In particular, the mechanism and characteristics of Li-ion batteries are presented, and different modelling methods are discussed. Considering that equivalent electric circuit models (EECMs) are the most widely used, a detailed analysis of the modelling procedure is presented.

Electrochemical Investigations into Blended Electrolytes Containing Ionic Liquids and a Glyme Solvent for Li-O­2 batteries

The Li-O2 battery may theoretically possess practical gravimetric energy densities several times greater than the current state-of-the-art Li-ion batteries.1 This magnitude of development is a requisite for true realization of electric vehicles capable of competing with the traditional combustion engine. However, significant challenges must be addressed before practical application may be considered. These include low efficiencies, low rate capabilities and the parasitic decomposition reactions of electrolyte/electrode materials resulting in very poor rechargeability.2-4 Ionic liquids, ILs, typically display several properties, extremely low vapor pressure and high electrochemical and thermal stability, which make them particularly interesting for Li-O2 battery electrolytes. However, the typically sluggish transport properties generally inhibit rate performance and cells suffer similar inefficiencies during cycling.5,6

In addition to the design of new ILs with tailored properties, formulating blended electrolytes using molecular solvents with ILs has been considered to improve their performance.7,8 In this work, we will discuss the physical properties vs. the electrochemical performance of a range of formulated electrolytes based on tetraglyme, a benchmark Li-O2 battery electrolyte solvent, and several ILs. The selected ILs are based on the bis{(trifluoromethyl)sulfonyl}imide anion and alkyl/ether functionalized cyclic alkylammonium cations, which exhibit very good stability and moderate viscosity.9 O2 electrochemistry will be investigated in these media using macro and microdisk voltammetry and O2 solubility/diffusivity is quantified as a function of the electrolyte formulation. Furthermore, galvanostatic cycling of selected electrolytes in Li-O2 cells will be discussed to probe their practical electrochemical performance. Finally, the physical characterization of the blended electrolytes will be reported in parallel to further determine structure (or formulation) vs. property relationships and to, therefore, assess the importance of certain electrolyte properties (viscosity, O2supply capability, donor number) on their performance.

This work was funded by the EPSRC (EP/L505262/1) and Innovate UK for the Practical Lithium-Air Batteries project (project number: 101577).

1. P. G. Bruce, S. A. Freunberger, L. J. Hardwick and J.-M. Tarascon, Nat. Mater., 11, 19 (2012).

2. S. A. Freunberger, Y. Chen, N. E. Drewett, L. J. Hardwick, F. Barde and P. G. Bruce, Angew. Chem., Int. Ed., 50, 8609 (2011).

3. B. D. McCloskey, A. Speidel, R. Scheffler, D. C. Miller, V. Viswanathan, J. S. Hummelshøj, J. K. Nørskov and A. C. Luntz, J. Phys. Chem. Lett., 3, 997 (2012).

4. D. G. Kwabi, T. P. Batcho, C. V. Amanchukwu, N. Ortiz-Vitoriano, P. Hammond, C. V. Thompson and Y. Shao-Horn, J. Phys. Chem. Lett., 5, 2850 (2014).

5. Z. H. Cui, W. G. Fan and X. X. Guo, J. Power Sources, 235, 251 (2013).

6. F. Soavi, S. Monaco and M. Mastragostino, J. Power Sources, 224, 115 (2013).

7. L. Cecchetto, M. Salomon, B. Scrosati and F. Croce, J. Power Sources, 213, 233 (2012).

8. A. Khan and C. Zhao, Electrochem. Commun., 49, 1 (2014).

9. Z. J. Chen, T. Xue and J.-M. Lee, RSC Adv., 2, 10564 (2012).

Changes in functional behavior of 93 % Pb(Mg1/3Nb2/3)O-3-7 % PbTiO3 thin films induced by ac electric fields

The dielectric properties of Au/[93%Pb(Mg1/3Nb2/3)O-3-7%PbTiO3] (PMN-PT)/(La0.5Sr0.5)CoO3/MgO thin-film capacitor heterostructures, made using pulsed laser deposition, have been investigated, with particular emphasis on the changes in response associated with increasing the magnitude of the ac measuring field. It was found that increasing the ac field caused a change in the frequency spectrum of relaxators, increasing the speed of response of "slow" relaxators, with an associated decrease in the freezing temperature (T-f) of the relaxor system; in addition, other characteristic parameters relating to polar relaxation (activation energy E-a and attempt frequency 1/tau(0)), described by fitting of the dielectric response to a Vogel-Fulcher expression, were found to change continuously as ac field levels were increased.

Dynamics of electric fields driving the laser acceleration of multi-MeV protons

The acceleration of multi-MeV protons from the rear surface of thin solid foils irradiated by an intense (similar to 10(18) W/cm(2)) and short (similar to 1.5 ps) laser pulse has been investigated using transverse proton probing. The structure of the electric field driving the expansion of the proton beam has been resolved with high spatial and temporal resolution. The main features of the experimental observations, namely, an initial intense sheath field and a late time field peaking at the beam front, are consistent with the results from particle-in-cell and fluid simulations of thin plasma expansion into a vacuum.

Impulsive electric fields driven by high-intensity interactions

The interaction of high-intensity laser pulses with matter releases instantaneously ultra-large currents of highly energetic electrons, leading to the generation of highly-transient, large-amplitude electric and magnetic fields. We report results of recent experiments in which such charge dynamics have been studied by using proton probing techniques able to provide maps of the electrostatic fields with high spatial and temporal resolution. The dynamics of ponderomotive channeling in underdense plasmas have been studied in this way, as also the processes of Debye sheath formation and MeV ion front expansion at the rear of laser-irradiated thin metallic foils. Laser-driven impulsive fields at the surface of solid targets can be applied for energy-selective ion beam focusing.