Battery Voltage Limit Analysis with Support Vector Machine and Fuzzy Logic


  • Róbert Szabolcsi Óbuda University, Budapest, Hungary
  • József Menyhárt University of Debrecen, Debrecen, Hungary



autonomous vehicle, UGV, SVM-Classification, fuzzy logic, battery


The effficiency and range of modern electric vehicles are crucial points in their design. Designers and engineers are highly motivated to find solutions to theses problems, or, as a rule, to improve existing electrical systems. Considerable number of modern batteries is available for use in electric vehicles and robots. The authors will propose new technology and new methods to use batteries with better efficiency. The authors propose to use the Support Vector Machine and Fuzzy logic in a new approach, which is the battery technical status management. The results show that it is possible to use these two methods simultaneously and they can ensure better results at the operation site.


POKORÁDI, L. and MENYHÁRT, J. Electric Vehicles’ Battery Parameter Tolerances Analysis by Fuzzy Logic. In Proceedings of the 11th IEEE International Symposium on Applied Computational Intelligence and Informatics SACI 2016. 2016, p. 361-364.

SZABOLCSI, R. and MENYHÁRT, J. Diagnostics of the Batteries Technical Status Using SVM Method. Revista Academiei Fortelor Terestre REVISTA, 2016, vol. 2, p. 190-197.

HONG-MO, D.Y. Just-In-Time Manufacturing, MGT2405, University of Toronto, Chapter 8, p. 1-13. [cited 2016-08-01]. Available from: <>.

KOOTANAEE, A.J., BABU, K.N., and TALARI, H.F. Just-in-Time Manufacturing System: From Introduction to Implement. International Journal of Economics, Business and Finance, 2013, vol. 1, No. 2, p. 7-25.

HOU, B., CHAN, H.K. and WANG, X. A Case Study of Just-In-Time System in the Chinese Automotive Industry. In Proceedings of the World Congress on Engineering 2011. London: WCE. 2011, vol. 1.

TORRES, D. Multi-Cell Li-Ion Battery Management System Using MSP430F5529 and bq76PL536 [Application Report]. MSP430 System Solutions, SLAA 2010, Texas Instruments.

Texas Instruments Ltd.: Battery Management Solutions, Harves the Power of the world’s energy, 2012. [cited 2016.08.01.] Available from: .

Rimac Automobili: Battery Management System. [cited 2016-08-01]. Available from: <>.

FISK, H. and LEIJGARD, J. A Battery Management Unit (in English) [Master Thesis]. Goteborg, Sweden: University of Gothenburg, 2010.

KLASS, V., BEHM, M. and LINDBERGH, G. State-of-health estimation of lithium-ion batteries in electric vehicles. T3:4 Battery system – Life model, Applied Electrochemistry, Department of Chemical Engineering and Technology, School of Chemical Science and Engineering, KTH Royal Institute of Technology, Swedish Hybrid Vehicle Centre, 2015.

RAHMAN, MD A. and ANWAR, S. Electrochemical Model Based Fault Diagnosis of a Lithium Ion Battery using Multiple Model Adaptive Estimation Approach, Industrial Technology (ICIT) IEEE, 2015, p. 210-217.

PÖYHÖNEN, S.: Support Vector Machines in fault diagnostics of electrical motors, [Research Report] Teknillinen Korkeakoulu, Tekniska Högskolan, Helsinki University of Technology, Control Engineering Laboratory, Espoo, 2002.

SZABOLCSI, R. Computer Aided Design of Modern Control Systems, Miklós Zrínyi National Defense University, 2011, 415 p.

POKORÁDI, L. Modelling of Systems and Signals (in Hungarian) Campus Publishing House, 2008, 242 p.

FAZEKAS, I. Neural Networks (in Hungarian) University of Debrecen, Hungary, 2013, p. 93-123¨.

MathWorks, [on line] Fuzzy Logic Toolbox [cited: 2013.11.28] Available from: <>.

SVM Tutorial [on line] Support Vector Regression with R [cited: 2016.01.05]. Available from: <>.

OWEN, W.J. The R Guide, Version 2.5, Department of Mathematics and Computer Science, University of Richmond, 2010.

XIE, K. Support Vector Machine, Concept and MATLAB Build, ECE 480, Team 4. Available from: <>

MathWorks [on line] File Exchange, SVM Demo (Richard Stapenhurst) [cited: 2016.03.20] Available from: <>

VAPNIK, V.N. Statistical Learning Theory, AT&T Research Laboratories, A Wiles Interscience Publication, John Wiley & Sons Inc, 1998. 740 p.

MENYHÁRT, J. Electric Voltage Measuring with Arduino Device, (in Hungarian) Hadmérnök Katonai Műszaki Tudományok Online, 2014, Vol. 4, p. 169-178.

MENYHÁRT, J. Concept of an UGV With Arduino Device, Hadmérnök Katonai Műszaki Tudományok Online, 2014, Vol. 2, p. 140-148.

JOHANYÁK, ZS.CS., KOVÁCS, SZ. On the Right Selection of the Fuzzy Membership Function, (in Hungarian) A GAMF Közleményei, Kecskemét, Hungary, 2004, Vol. 19, p. 73-84.

HAN, J., MORAGA, C. The Influence of the Sigmoid Function Parameters on the Speed of Backpropagation learning, Computational Models of Neurons and Neural Nets, From Natural to Artificial Neural Computation, 2005, vol. 930, p. 195-201.

GIBBS, M.N., MACKAY, D.J.C. Variational Gaussian process classifiers, IEEE Transactions on Neural Networks, 2000, vol. 11, Issue 6, 2000, p. 1-12.

HEEGER, D. The logistic function, handsout, [on line] [cited: 2016.09.12.] Available from: <>






Research Paper


How to Cite

Battery Voltage Limit Analysis with Support Vector Machine and Fuzzy Logic. (2017). Advances in Military Technology, 12(1), 21-32.

Similar Articles

1-10 of 53

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)