Ion-Atom/Argon - Calculation of ionization cross sections by fast ion impact for neutral target atoms ranging from hydrogen to argon

A FORTRAN 90 program is presented which calculates the total cross sections, and the electron energy spectra of the singly and doubly differential cross sections for the single target ionization of neutral atoms ranging from hydrogen up to and including argon. The code is applicable for the case of both high and low Z projectile impact in fast ion-atom collisions. The theoretical models provided for the program user are based on two quantum mechanical approximations which have proved to be very successful in the study of ionization in ion-atom collisions. These are the continuum-distorted-wave (CDW) and continuum-distorted-wave eikonal-initial-state (CDW-EIS) approximations. The codes presented here extend previously published. codes for single ionization of. target hydrogen [Crothers and McCartney, Comput. Phys. Commun. 72 (1992) 288], target helium [Nesbitt, O'Rourke and Crothers, Comput. Phys. Commun. 114 (1998) 385] and target atoms ranging from lithium to neon [O'Rourke, McSherry and Crothers, Comput. Phys. Commun. 131 (2000) 129]. Cross sections for all of these target atoms may be obtained as limiting cases from the present code. Title of program: ARGON Catalogue identifier: ADSE Program summary URL: http://cpc.cs.qub.ac.uk/cpc/summaries/ADSE Program obtainable from: CPC Program Library Queen's University of Belfast, N. Ireland Licensing provisions: none Computer for which the program is designed and others on which it is operable: Computers: Four by 200 MHz Pro Pentium Linux server, DEC Alpha 21164; Four by 400 MHz Pentium 2 Xeon 450 Linux server, IBM SP2 and SUN Enterprise 3500 Installations: Queen's University, Belfast Operating systems under which the program has been tested: Red-hat Linux 5.2, Digital UNIX Version 4.0d, AIX, Solaris SunOS 5.7 Compilers: PGI workstations, DEC CAMPUS Programming language used: FORTRAN 90 with MPI directives No. of bits in a word: 64, except on Linux servers 32 Number of processors used: any number Has the code been vectorized or parallelized? Parallelized using MPI No. of bytes in distributed program, including test data, etc.: 32 189 Distribution format: tar gzip file Keywords: Single ionization, cross sections, continuum-distorted-wave model, continuum- distorted-wave eikonal-initial-state model, target atoms, wave treatment Nature of physical problem: The code calculates total, and differential cross sections for the single ionization of target atoms ranging from hydrogen up to and including argon by both light and heavy ion impact. Method of solution: ARGON allows the user to calculate the cross sections using either the CDW or CDW-EIS [J. Phys. B 16 (1983) 3229] models within the wave treatment. Restrictions on the complexity of the program: Both the CDW and CDW-EIS models are two-state perturbative approximations. Typical running time: Times vary according to input data and number of processors. For one processor the test input data for double differential cross sections (40 points) took less than one second, whereas the test input for total cross sections (20 points) took 32 minutes. Unusual features of the program: none (C) 2003 Elsevier B.V All rights reserved.

Viscosity and Carbon Dioxide Solubility for LiPF6, LiTFSI, and LiFAP in Alkyl Carbonates: Lithium Salt Nature and Concentration Effect

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.

Real-time estimation of battery internal temperature based on a simplified thermoelectric model

Li-ion batteries have been widely used in the EVs, and the battery thermal management is a key but challenging part of the battery management system. For EV batteries, only the battery surface temperature can be measured in real-time. However, it is the battery internal temperature that directly affects the battery performance, and large temperature difference may exist between surface and internal temperatures, especially in high power demand applications. In this paper, an online battery internal temperature estimation method is proposed based on a novel simplified thermoelectric model. The battery thermal behaviour is first described by a simplified thermal model, and battery electrical behaviour by an electric model. Then, these two models are interrelated to capture the interactions between battery thermal and electrical behaviours, thus offer a comprehensive description of the battery behaviour that is useful for battery management. Finally, based on the developed model, the battery internal temperature is estimated using an extended Kalman filter. The experimental results confirm the efficacy of the proposed method, and it can be used for online internal temperature estimation which is a key indicator for better real-time battery thermal management.

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).

Effect of the Electrolyte Formulation on the Performances of Edlc and Li-Ion Batteries

One of the most important components in electrochemical storage devices (batteries and supercapacitors) is undoubtedly the electrolyte. The basic function of any electrolyte in these systems is the transport of ions between the positive and negative electrodes. In addition, electrochemical reactions occurring at each electrode/electrolyte interface are the origin of the current generated by storage devices. In other words, performances (capacity, power, efficiency and energy) of electrochemical storage devices are strongly related to the electrolyte properties, as well as, to the affinity for the electrolyte to selected electrode materials. Indeed, the formulation of electrolyte presenting good properties, such as high ionic conductivity and low viscosity, is then required to enhance the charge transfer reaction at electrode/electrolyte interface (e.g. charge accumulation in the case of Electrochemical Double Layer Capacitor, EDLC). For practical and safety considerations, the formulation of novel electrolytes presenting a low vapor pressure, a large liquid range temperature, a good thermal and chemical stabilities is also required.

This lecture will be focused on the effect of the electrolyte formulation on the performances of electrochemical storage devices (Li-ion batteries and supercapacitors). During which, a summary of the physical, thermal and electrochemical data obtained by our group, recently, on the formulation of novel electrolyte-based on the mixture of an ionic liquid (such as EmimNTf2 and Pyr14NTf2) and carbonate or dinitrile solvents will be presented and commented. The impact of the electrolyte formulation on the storage performances of EDLC and Li-ion batteries will be also discussed to further understand the relationship between electrolyte formulation and electrochemical performances. This talk will also be an opportunity to further discuss around the effects of additives (SEI builder: fluoroethylene carbonate and vinylene carbonate), ionic liquids, structure and nature of lithium salt (LiTFSI vs LiPF6) on the cyclability of negative electrode to then enhance the electrolyte formulation. For that, our recent results on TiSnSb and graphite negative electrodes will be presented and discussed, for example 1,2.

1-C. Marino, A. Darwiche1, N. Dupré, H.A. Wilhelm, B. Lestriez, H. Martinez, R. Dedryvère, W. Zhang, F. Ghamouss, D. Lemordant, L. Monconduit “ Study of the Electrode/Electrolyte Interface on Cycling of a Conversion Type Electrode Material in Li Batteries” J. Phys.chem. C, 2013, 117, 19302-19313

2- Mouad Dahbi, Fouad Ghamouss, Mérièm Anouti, Daniel Lemordant, François Tran-Van “Electrochemical lithiation and compatibility of graphite anode using glutaronitrile/dimethyl carbonate mixtures containing LiTFSI as electrolyte” 2013, 43, 4, 375-385.

Improved Real-time State-of-Charge Estimation of LiFePO4 Battery Based on a Novel Thermoelectric Model

Li-ion batteries have been widely used in electric vehicles, and battery internal state estimation plays an important role in the battery management system. However, it is technically challenging, in particular, for the estimation of the battery internal temperature and state-ofcharge (SOC), which are two key state variables affecting the battery performance. In this paper, a novel method is proposed for realtime simultaneous estimation of these two internal states, thus leading to a significantly improved battery model for realtime SOC estimation. To achieve this, a simplified battery thermoelectric model is firstly built, which couples a thermal submodel and an electrical submodel. The interactions between the battery thermal and electrical behaviours are captured, thus offering a comprehensive description of the battery thermal and electrical behaviour. To achieve more accurate internal state estimations, the model is trained by the simulation error minimization method, and model parameters are optimized by a hybrid optimization method combining a meta-heuristic algorithm and the least square approach. Further, timevarying model parameters under different heat dissipation conditions are considered, and a joint extended Kalman filter is used to simultaneously estimate both the battery internal states and time-varying model parameters in realtime. Experimental results based on the testing data of LiFePO4 batteries confirm the efficacy of the proposed method.