On determining productive capacity of EV traction battery repair area

User Rating:  / 0
PoorBest 

Authors:

L.S.Koriashkina, Cand. Sc. (Tech.), Assoc. Prof., orcid.org/0000-0001-6423-092X, Dnipro University of Technology, Dnipro, Ukraine, e-maill: This email address is being protected from spambots. You need JavaScript enabled to view it.

O.V.Deryugin, Cand. Sc. (Tech.), Assoc. Prof., orcid.org/0000-0002-2456-7664, Dnipro University of Technology, Dnipro, Ukraine, e-maill: This email address is being protected from spambots. You need JavaScript enabled to view it.

S.O.Fedoriachenko, Cand. Sc. (Tech.), orcid.org/0000-0002-8512-3493, Dnipro University of Technology, Dnipro, Ukraine, e-maill: This email address is being protected from spambots. You need JavaScript enabled to view it.

S.I.Cheberiachko, Dr. Sc. (Tech.), Prof., orcid.org/0000-0003-3281-7157, Dnipro University of Technology, Dnipro, Ukraine, e-maill: This email address is being protected from spambots. You need JavaScript enabled to view it.

M.A.Vesela, Cand. Sc. (Tech.), orcid.org/0000-0001-9318-9110, Kharkiv National Automobile and Highway University, Kharkiv, Ukraine

Abstract:

Purpose. Toensure the effective functioning of the area for diagnosis and repair of tractive electric vehicle (EV) battery by determining the technical and economic indicators of the production capacity of the enterprise.

Methodology. Modelling the process of repairing section of the EV traction battery is performed using the main principles of the queuing system, the graph theory. While calculating the probability-time characteristics and determining the production capacity of the site, the methods of mathematical statistics and probability theory were involved.

Findings. With the use of systematic approach, the EV market in Ukraine and the dynamics of the demand over the past three years have been studied. There was collected and analyzed information about issues related to the EV operation, and the most common ones were identified. Empirically, there was obtained an estimate for the duration of the process of repairing and replacing the EV traction battery, as well as the cost of basic operations. The battery repair section of the traction battery is represented as a multichannel queuing system, assuming that the flow of applications for diagnosing and repair is Poisson’s flow, and the random process is Markovian. The problem of rational organization of the EV traction battery repair site has been solved by determining the minimum number of repair posts, at which the queue will not grow indefinitely (if customers can wait for their service for a long time and do not leave the queue). The number of repair posts was estimated for the traction battery, which in the existing conditions will allow organizing technical customer service effectively, minimizing the total economic losses from downtime of process equipment and refusals of applications for repairs.

Originality. The use of models and methods of the queuing system for the calculation of technical and economic indicators of the production capacity of the traction battery repair section allowed taking into account the stochastic nature of the needs for repaire, and determining the optimal values of those characteristics that ensure the efficient performance of the EV repair section.

Practical value. The proposed method for determining the production capacity of the repair section of traction battery is used in solving problems related to the selection and rational placement of technological equipment when designing or upgrading a workshop.

References.

1. Hlushkova, D. B., Hrinchenko, O. D., Kostina, L. L., & Cholodov, A. P. (2018). The choice of material for strengthening of leading edges of working blades of steam turbines. Problems of Atomic Science and Technology, 113(1), 181-188.

2. Franchuk, V. P., Ziborov, K. A., Krivda, V. V., & Fedoria­chenko, S. O. (2018),. Influence of thermophysical processes on the friction properties of wheel – rail pair in the contact area. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 2, 46-52, DOI: 10.29202/nvngu/2018-2/7.

3. Raksha, S. V., Anofriev, P. G., Bohomaz, V. M., & Kuropiat­nyk, O. S. (2019) Mathematical and S-models of cargo oscillations during movement of bridge crane. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 2, 108-115, DOI: 10.29202/nvngu/2019-2/16.

4. Peng-Yong Kong, & Karagiannidis, K. (2016). Charging Schemes for Plug-In Hybrid Electric Vehicles in Smart Gride: A Survey. IEEE Access Journal, 1-29. DOI: 10.1109/ACCESS.2016.2614689.

5. Pivnyak, G., Dychkovskyi, R., Bobyliov, O., Cabana, E. C., & Smoliński, A. (2018). Mathematical and geomechanical model in physical and chemical processes of underground coal gasification. Solid State Phenomena, 277, 1-16. DOI: 10.4028/www.scientific.net/SSP.277.1.

6. Sparacino, A. R., Grainger, B. M., Kerestes, R. J., & Reed, G. F. (2012). Design and Simulation of a DC Electric Vehicle Charging Station Connected to a MVDC Infrastructure. Energy Conversion Congress and Exposition (ECCE) (pp. 1168-1175). DOI: 10.1109/ECCE.2012.642685.

7. Kravets, V. V., Kravets, T. V., & Kharchenko, A. V. (2009). Using quaternion matrices to describe the kinematics and nonlinear dynamics of an asymmetric rigid body. International Applied Mechanics, 45(2), 223-231.

8. Kravets, V. V., Sakhno, V., Bas, K., & Kravets, V. I. (2018). Program spatial movement of high-speed vehicles. IOP Conference. Series: Materials Science and Engineering, 383(1), 012032, DOI: 10.1088/1757-899X/383/1/012032.

9. He, S. Y., Kuo, Y.-H., & Wu, D. (2016). Incorporating in situational and spatial factors in these lection of the optimal locations of public electric vehicle charging facilities: A case study of Beijing, China. Transportation Research Part C: Emerging Technologies, 67, 131-148. DOI: 10.1016/j.trc.2016.02.003.

10. Ziborov, K., & Fedoriachenko, S. (2014), The frictional work in pair wheel-rail in case of different structural scheme of mining rolling stock. Progressive Technologies of Coal, Coalbed Methane, and Ores Mining, 517-521.

11. Deryugin, O. V., & Cheberyachko, S. I. (2015). Substantiation of the choice of a truck on the criterion of minimizing the psychophysiological load on the driver. Vostochno-Evropeiskii zhurnal peredovykh tekhnologii, 3(75), 15-22.

12. Chaofei Lu, Xi’an Sun, & Jingru Zhao (2015). The optimization research on the queuing system in highway long-distance bus terminal small express. International Conference on Logistics, Informatics and Service Sciences (LISS) (pp. 1-4), Barcelona. DOI: 10.1109/LISS.2015.7369803.

13. Matsyuk, I., & Shlyahov, E. (2015), The research of plane link complexstructure mechanisms by vector algebra methods. Eastern-European Journal of Enterprise Technologies, 3(7), 34-38.

14. Golinko, V. I., Cheberyachko, S. I., Yavorskaya, E. A., & Cheberyachko, Y. I. (2016). Analysis of protective value of dust-fighting respirators and its effect on dust burden of miners. Gornyi Zhurnal, 3, 76-80. DOI 10.17580/gzh.2016.03.16.

15. Laukhin, D. V., Beketov, O. V., Rott, N. O., Tyuterev, I. A., Ivantsov, S. V., & Laukhin, V. D. (2017). The Analysis of Interrelation between Kinetics of Propagation of Plastic Deformation and Initiation of Ductile Fracture. Metallofiz. Noveishie Tekhnol., 39(10), 1335-1343. DOI: 10.15407/mfint.39.10.1335.

16. Beshta, A., Aziukovskyi, O., Balakhontsev, A., & Shestakov, A. (2017). Combined power electronic converter for simultaneous operation of several renewable energy sources. 2017 International Conference on Modern Electrical and Energy Systems (MEES) (pp. 236-239), Kremenchuk. DOI: 10.1109/MEES.2017.8248898.

17. Berezshnaya, O. V., Gribkov, E. P., Borovik, P. V., & Kas­sov, V. D. (2019). The Finite Element Modulation of Thermostressed State of Coating Formation at Electric Contact Surfacing of “Shaft” Type Parts. Advances in Materials Science and Engineering, 1-18. Article ID 7601792. DOI: 10.1155/2019/7601792.

18. Koišová, E., Grmanová, E., & Habánik, J. (2018). Regional disparities in financing innovations in small and medium-sized enterprises. Journal of International Studies, 11(3), 124-136. DOI: 10.14254/2071-8330.2018/11-3/11.

19. Syrotkina, O., Alekseyev, M., & Aleksieiev, O. (2017). Evaluation to determine the efficiency for the diagnosis search formation method of failures in automated systems. Eastern-European Journal of Enterprise Technologies, 4(9(88)), 59-68. DOI: 10.15587/1729-4061.2017.108454.

20. Diachenko, G. G., & Aziukovskyi, O. O. (2017). Investigation of the Process Parameters Influence on the Energy Efficiency of an Induction Motor under Model Predictive Control GRAMPC. Mechanics, Materials Sicence & Engineering, 12, 124-132. ISSN 2412-5954.

 повний текст / full article

Visitors

2946182
Today
This Month
All days
94
6568
2946182

Guest Book

If you have questions, comments or suggestions, you can write them in our "Guest Book"

Registration data

ISSN (print) 2071-2227,
ISSN (online) 2223-2362.
Journal was registered by Ministry of Justice of Ukraine.
Registration number КВ No.17742-6592PR dated April 27, 2011.

Contacts

D.Yavornytskyi ave.,19, pavilion 3, room 24-а, Dnipro, 49005
Tel.: +38 (056) 746 32 79.
e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
You are here: Home About the journal contact EngCat Archive 2019 Contens №5 2019 Power Supply Technologies On determining productive capacity of EV traction battery repair area