Modelling and Simulation of Vehicle Electric Power Battery System

Harrison Obiora Amuji; Donatus Eberechukwu Onwuegbuchunam; Moses Olatunde Aponjolosun; Kenneth Okechukwu Okeke; Justice Chigozie Mbachu; John Folayan Ojutalayo

doi:doi:10.11648/j.ijtet.20220804.11

Modelling and Simulation of Vehicle Electric Power Battery System

Harrison Obiora Amuji, Donatus Eberechukwu Onwuegbuchunam, Moses Olatunde Aponjolosun, Kenneth Okechukwu Okeke, Justice Chigozie Mbachu, John Folayan Ojutalayo

Published in International Journal of Transportation Engineering and Technology (Volume 8, Issue 4)

Received: 12 November 2022 Accepted: 14 December 2022 Published: 31 December 2022

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Abstract

Electric power battery will continue to play significant role in electrifying of transportation systems as its capacity to store clean energy and provide reliable power is continually being improved through research and development. In a related vein electric power batteries for vehicles and other applications are considered viable alternative energy solutions in the global quest to mitigate the impact of greenhouse effect. Documented evidence shows that enormous resources are being invested by organizations toward development of batteries with higher energy density, longer life, faster and more efficient load. Thus, as national governments continue to legislate on climate change, more productive investments in research and development will ultimately favour the adoption of efficient and sustainable electric battery powered mobility solutions in the long run. Developing efficient and sustainable energy solution requires that electric power batteries’ performance should be reliable. Thus, the need to build reliable electric power battery system has provided the impetus to this study. In order to accurately study the performance of electric powered batteries, an equivalent circuit model is usually simulated to analyse dynamic characteristics and contrast with different order models of the battery. Using experiments the parameters of the battery are calibrated for improved efficiency. In this paper, we designed, developed and fitted the electric power battery model through electromagnetic induction and estimated its parameters; the battery will be recharged after a given period of time when the power is exhausted. The reliability model for the electric power battery was determined using the non-parametric method. We developed a simple linear regression model and through it, estimated the parameters of the fitted probability model, the mean time to failure and the reliability of the battery. We determined the mean time to failure of the power battery to be 477.12 hours, which is approximately 20days; and the reliability of the power battery is 3.609 x 10^-71. We found and fitted the probability distribution for the electric power battery system and determined the output for each loop as presented in Table 2 by the Y* column. The developed and fitted regression model is used to forecast for the performance and future output of the power battery system.

Published in	International Journal of Transportation Engineering and Technology (Volume 8, Issue 4)
DOI	10.11648/j.ijtet.20220804.11
Page(s)	50-56
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Electric Power Battery, Electromagnetic Induction, Electric Vehicle, Reliability, Simulation, Probability Distribution

References

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[2]	Amuji, H. O. and Umelo-Ibemere, N. C. (2015). Comparison of the Reliability of Dry Cell Batteries: A Case Study of Flash and Tiger Head Batteries. Journal of the Nigerian Association of Mathematical Physics, Vol. 31, July 2015, Pp 413-418.
[3]	Ugwuanyim, G. U, Amuji H. O. Bartholomew, D. C and Wisdom, H. (2021). The reliability of ART in the control of human immune virus (HIV). Advances and Applications in Statistics, Vol. 67 (2), Pp 117-132.
[4]	Bhovi1, R. P, Ranjith, A. C, Sachin, K. M & Kariyappa B. S (2021). Modeling and Simulation of Battery Management System (BMS) for Electric Vehicles, Journal of University of Shanghai for Science and Technology, Vol. 23, No (6).
[5]	Hu, R., (2011) Battery Management Systems for Electric Vehicle Applications. Unpublished M.Sc thesis Department of Electrical and Computer Engineering, University of Windsor, Canada.
[6]	Qin, D., Li, J., Wang, T., and Zhang, D. (2019). Modeling and Simulating a Battery for an Electric Vehicle Based on Modelica. Automotive Innovation (2019) 2: 169–177 https://doi.org/10.1007/s42154-019-00066-0
[7]	Sagaria, S. (2020), A Modeling Approach for Assessing Energy Performance and Influential Factors of Vehicles Powered by Battery, Fuel cell and Ultracapacitor. Unpublished M.Sc thesis Instituto Superior Técnico MHSE, University of Lisbon.
[8]	Hanifah, R. A Toha, S. F and S Ahmad, S. (2015), Electric Vehicle Battery Modelling and Performance Comparison in Relation to Range Anxiety, Procedia Computer Science, 76 250 – 256.
[9]	Lewis, E. E. (1987). Introduction to Reliability Engineering. John Wiley and Sons, Singapore.
[10]	Smith, R. L., Crowder, M. J., Kimber, A. C., and Sweeting, T. J. (1991). Statistical Analysis of Reliability Data 1st Ed., Chapman and Hall, Great Britain.
[11]	Irwin, M and John, E. F. (1987). Probability and Statistics for Engineers 3rd Ed, Prentice-Hall of India Private Ltd, India, p 470.
[12]	Billinton, R and Alan, R. N. (1983). Reliability Evaluation of Engineering System, Plenum Press, New York.
[13]	Barlow, R. E. and Proschan, F. (1981). Statistical Theory of Reliability and Life Testing 2nd Ed. Silver Spring, Mary Land.
[14]	Arua, A. I., Chigbu, P. E., Chukwu, W. I. E., Ezekwem, C. C. and Okafor, F. C. (2000). Advanced Statistics for Higher Education Vol. 1. Academic Publishers, Nsukka Nigeria.
[15]	Shooman, M. L. (1968). Probabilistic Reliability, An Engineering Approach. Mc Graw-Hill, New York.
[16]	Weiss, N. O (1966). The Expulsion of Magnetic Flux by Eddies, Proceedings of the Royal Society of London Series A, Mathematical and Physical Sciences Vol. 293 (1434), pp. 310-328.
[17]	Amuji, H. O, Moneke, U. U Igboanusi, C and Onukwube, O. G (2022), Modelling the generation/transmission and distribution of electricity, Far East Journal of Applied Mathematics Vol. 114, Pp 49-64.
[18]	McAtee, R (2018). Solar Photovoltaic Reliability after Hurricanes, The Military Engineer Vol. 110 (713), pp. 64-66.
[19]	Tobias, P. A. and Trindade, D. C. (1995) Applied Reliability. 2nd Edition, Chapman and Hall/CRC, New York.
[20]	Ruggeri, F. and Siva, S. (2005) On Modelling Change Points in Non-Homogeneous Poisson Process. Statistical Inference for Stochastic Processes, 8, Pp. 311-329.
[21]	Amuji, H. O; Ogbonna, C. J; Ugwuanyim, G. U; Iwu, H. C. and Okechukwu, B. N. Optimal Water Pipe Replacement Policy. Open Journal of Optimization, Vol. 7 No. 2, June, 2018, Pp 41 – 49.

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APA Style

Harrison Obiora Amuji, Donatus Eberechukwu Onwuegbuchunam, Moses Olatunde Aponjolosun, Kenneth Okechukwu Okeke, Justice Chigozie Mbachu, et al. (2022). Modelling and Simulation of Vehicle Electric Power Battery System. International Journal of Transportation Engineering and Technology, 8(4), 50-56. https://doi.org/10.11648/j.ijtet.20220804.11

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ACS Style

Harrison Obiora Amuji; Donatus Eberechukwu Onwuegbuchunam; Moses Olatunde Aponjolosun; Kenneth Okechukwu Okeke; Justice Chigozie Mbachu, et al. Modelling and Simulation of Vehicle Electric Power Battery System. Int. J. Transp. Eng. Technol. 2022, 8(4), 50-56. doi: 10.11648/j.ijtet.20220804.11

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AMA Style

Harrison Obiora Amuji, Donatus Eberechukwu Onwuegbuchunam, Moses Olatunde Aponjolosun, Kenneth Okechukwu Okeke, Justice Chigozie Mbachu, et al. Modelling and Simulation of Vehicle Electric Power Battery System. Int J Transp Eng Technol. 2022;8(4):50-56. doi: 10.11648/j.ijtet.20220804.11

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@article{10.11648/j.ijtet.20220804.11,
  author = {Harrison Obiora Amuji and Donatus Eberechukwu Onwuegbuchunam and Moses Olatunde Aponjolosun and Kenneth Okechukwu Okeke and Justice Chigozie Mbachu and John Folayan Ojutalayo},
  title = {Modelling and Simulation of Vehicle Electric Power Battery System},
  journal = {International Journal of Transportation Engineering and Technology},
  volume = {8},
  number = {4},
  pages = {50-56},
  doi = {10.11648/j.ijtet.20220804.11},
  url = {https://doi.org/10.11648/j.ijtet.20220804.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijtet.20220804.11},
  abstract = {Electric power battery will continue to play significant role in electrifying of transportation systems as its capacity to store clean energy and provide reliable power is continually being improved through research and development. In a related vein electric power batteries for vehicles and other applications are considered viable alternative energy solutions in the global quest to mitigate the impact of greenhouse effect. Documented evidence shows that enormous resources are being invested by organizations toward development of batteries with higher energy density, longer life, faster and more efficient load. Thus, as national governments continue to legislate on climate change, more productive investments in research and development will ultimately favour the adoption of efficient and sustainable electric battery powered mobility solutions in the long run. Developing efficient and sustainable energy solution requires that electric power batteries’ performance should be reliable. Thus, the need to build reliable electric power battery system has provided the impetus to this study. In order to accurately study the performance of electric powered batteries, an equivalent circuit model is usually simulated to analyse dynamic characteristics and contrast with different order models of the battery. Using experiments the parameters of the battery are calibrated for improved efficiency. In this paper, we designed, developed and fitted the electric power battery model through electromagnetic induction and estimated its parameters; the battery will be recharged after a given period of time when the power is exhausted. The reliability model for the electric power battery was determined using the non-parametric method. We developed a simple linear regression model and through it, estimated the parameters of the fitted probability model, the mean time to failure and the reliability of the battery. We determined the mean time to failure of the power battery to be 477.12 hours, which is approximately 20days; and the reliability of the power battery is 3.609 x 10-71. We found and fitted the probability distribution for the electric power battery system and determined the output for each loop as presented in Table 2 by the Y* column. The developed and fitted regression model is used to forecast for the performance and future output of the power battery system.},
 year = {2022}
}

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TY  - JOUR
T1  - Modelling and Simulation of Vehicle Electric Power Battery System
AU  - Harrison Obiora Amuji
AU  - Donatus Eberechukwu Onwuegbuchunam
AU  - Moses Olatunde Aponjolosun
AU  - Kenneth Okechukwu Okeke
AU  - Justice Chigozie Mbachu
AU  - John Folayan Ojutalayo
Y1  - 2022/12/31
PY  - 2022
N1  - https://doi.org/10.11648/j.ijtet.20220804.11
DO  - 10.11648/j.ijtet.20220804.11
T2  - International Journal of Transportation Engineering and Technology
JF  - International Journal of Transportation Engineering and Technology
JO  - International Journal of Transportation Engineering and Technology
SP  - 50
EP  - 56
PB  - Science Publishing Group
SN  - 2575-1751
UR  - https://doi.org/10.11648/j.ijtet.20220804.11
AB  - Electric power battery will continue to play significant role in electrifying of transportation systems as its capacity to store clean energy and provide reliable power is continually being improved through research and development. In a related vein electric power batteries for vehicles and other applications are considered viable alternative energy solutions in the global quest to mitigate the impact of greenhouse effect. Documented evidence shows that enormous resources are being invested by organizations toward development of batteries with higher energy density, longer life, faster and more efficient load. Thus, as national governments continue to legislate on climate change, more productive investments in research and development will ultimately favour the adoption of efficient and sustainable electric battery powered mobility solutions in the long run. Developing efficient and sustainable energy solution requires that electric power batteries’ performance should be reliable. Thus, the need to build reliable electric power battery system has provided the impetus to this study. In order to accurately study the performance of electric powered batteries, an equivalent circuit model is usually simulated to analyse dynamic characteristics and contrast with different order models of the battery. Using experiments the parameters of the battery are calibrated for improved efficiency. In this paper, we designed, developed and fitted the electric power battery model through electromagnetic induction and estimated its parameters; the battery will be recharged after a given period of time when the power is exhausted. The reliability model for the electric power battery was determined using the non-parametric method. We developed a simple linear regression model and through it, estimated the parameters of the fitted probability model, the mean time to failure and the reliability of the battery. We determined the mean time to failure of the power battery to be 477.12 hours, which is approximately 20days; and the reliability of the power battery is 3.609 x 10-71. We found and fitted the probability distribution for the electric power battery system and determined the output for each loop as presented in Table 2 by the Y* column. The developed and fitted regression model is used to forecast for the performance and future output of the power battery system.
VL  - 8
IS  - 4
ER  -

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Author Information

Harrison Obiora Amuji

Department of Statistics, Federal University of Technology, Owerri, Nigeria
Donatus Eberechukwu Onwuegbuchunam

Department of Maritime Management Technology, Federal University of Technology, Owerri, Nigeria
Moses Olatunde Aponjolosun

Department of Maritime Management Technology, Federal University of Technology, Owerri, Nigeria
Kenneth Okechukwu Okeke

Department of Maritime Management Technology, Federal University of Technology, Owerri, Nigeria
Justice Chigozie Mbachu

Department of Maritime Management Technology, Federal University of Technology, Owerri, Nigeria
John Folayan Ojutalayo

Department of Nautical Science, Federal College of Fisheries and Marine Technology, Lagos, Nigeria

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APA Style

Harrison Obiora Amuji, Donatus Eberechukwu Onwuegbuchunam, Moses Olatunde Aponjolosun, Kenneth Okechukwu Okeke, Justice Chigozie Mbachu, et al. (2022). Modelling and Simulation of Vehicle Electric Power Battery System. International Journal of Transportation Engineering and Technology, 8(4), 50-56. https://doi.org/10.11648/j.ijtet.20220804.11

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ACS Style

Harrison Obiora Amuji; Donatus Eberechukwu Onwuegbuchunam; Moses Olatunde Aponjolosun; Kenneth Okechukwu Okeke; Justice Chigozie Mbachu, et al. Modelling and Simulation of Vehicle Electric Power Battery System. Int. J. Transp. Eng. Technol. 2022, 8(4), 50-56. doi: 10.11648/j.ijtet.20220804.11

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AMA Style

Harrison Obiora Amuji, Donatus Eberechukwu Onwuegbuchunam, Moses Olatunde Aponjolosun, Kenneth Okechukwu Okeke, Justice Chigozie Mbachu, et al. Modelling and Simulation of Vehicle Electric Power Battery System. Int J Transp Eng Technol. 2022;8(4):50-56. doi: 10.11648/j.ijtet.20220804.11

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@article{10.11648/j.ijtet.20220804.11,
  author = {Harrison Obiora Amuji and Donatus Eberechukwu Onwuegbuchunam and Moses Olatunde Aponjolosun and Kenneth Okechukwu Okeke and Justice Chigozie Mbachu and John Folayan Ojutalayo},
  title = {Modelling and Simulation of Vehicle Electric Power Battery System},
  journal = {International Journal of Transportation Engineering and Technology},
  volume = {8},
  number = {4},
  pages = {50-56},
  doi = {10.11648/j.ijtet.20220804.11},
  url = {https://doi.org/10.11648/j.ijtet.20220804.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijtet.20220804.11},
  abstract = {Electric power battery will continue to play significant role in electrifying of transportation systems as its capacity to store clean energy and provide reliable power is continually being improved through research and development. In a related vein electric power batteries for vehicles and other applications are considered viable alternative energy solutions in the global quest to mitigate the impact of greenhouse effect. Documented evidence shows that enormous resources are being invested by organizations toward development of batteries with higher energy density, longer life, faster and more efficient load. Thus, as national governments continue to legislate on climate change, more productive investments in research and development will ultimately favour the adoption of efficient and sustainable electric battery powered mobility solutions in the long run. Developing efficient and sustainable energy solution requires that electric power batteries’ performance should be reliable. Thus, the need to build reliable electric power battery system has provided the impetus to this study. In order to accurately study the performance of electric powered batteries, an equivalent circuit model is usually simulated to analyse dynamic characteristics and contrast with different order models of the battery. Using experiments the parameters of the battery are calibrated for improved efficiency. In this paper, we designed, developed and fitted the electric power battery model through electromagnetic induction and estimated its parameters; the battery will be recharged after a given period of time when the power is exhausted. The reliability model for the electric power battery was determined using the non-parametric method. We developed a simple linear regression model and through it, estimated the parameters of the fitted probability model, the mean time to failure and the reliability of the battery. We determined the mean time to failure of the power battery to be 477.12 hours, which is approximately 20days; and the reliability of the power battery is 3.609 x 10-71. We found and fitted the probability distribution for the electric power battery system and determined the output for each loop as presented in Table 2 by the Y* column. The developed and fitted regression model is used to forecast for the performance and future output of the power battery system.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Modelling and Simulation of Vehicle Electric Power Battery System
AU  - Harrison Obiora Amuji
AU  - Donatus Eberechukwu Onwuegbuchunam
AU  - Moses Olatunde Aponjolosun
AU  - Kenneth Okechukwu Okeke
AU  - Justice Chigozie Mbachu
AU  - John Folayan Ojutalayo
Y1  - 2022/12/31
PY  - 2022
N1  - https://doi.org/10.11648/j.ijtet.20220804.11
DO  - 10.11648/j.ijtet.20220804.11
T2  - International Journal of Transportation Engineering and Technology
JF  - International Journal of Transportation Engineering and Technology
JO  - International Journal of Transportation Engineering and Technology
SP  - 50
EP  - 56
PB  - Science Publishing Group
SN  - 2575-1751
UR  - https://doi.org/10.11648/j.ijtet.20220804.11
AB  - Electric power battery will continue to play significant role in electrifying of transportation systems as its capacity to store clean energy and provide reliable power is continually being improved through research and development. In a related vein electric power batteries for vehicles and other applications are considered viable alternative energy solutions in the global quest to mitigate the impact of greenhouse effect. Documented evidence shows that enormous resources are being invested by organizations toward development of batteries with higher energy density, longer life, faster and more efficient load. Thus, as national governments continue to legislate on climate change, more productive investments in research and development will ultimately favour the adoption of efficient and sustainable electric battery powered mobility solutions in the long run. Developing efficient and sustainable energy solution requires that electric power batteries’ performance should be reliable. Thus, the need to build reliable electric power battery system has provided the impetus to this study. In order to accurately study the performance of electric powered batteries, an equivalent circuit model is usually simulated to analyse dynamic characteristics and contrast with different order models of the battery. Using experiments the parameters of the battery are calibrated for improved efficiency. In this paper, we designed, developed and fitted the electric power battery model through electromagnetic induction and estimated its parameters; the battery will be recharged after a given period of time when the power is exhausted. The reliability model for the electric power battery was determined using the non-parametric method. We developed a simple linear regression model and through it, estimated the parameters of the fitted probability model, the mean time to failure and the reliability of the battery. We determined the mean time to failure of the power battery to be 477.12 hours, which is approximately 20days; and the reliability of the power battery is 3.609 x 10-71. We found and fitted the probability distribution for the electric power battery system and determined the output for each loop as presented in Table 2 by the Y* column. The developed and fitted regression model is used to forecast for the performance and future output of the power battery system.
VL  - 8
IS  - 4
ER  -

Copy | Download