Validation of eAircraft battery simulation approach using field measurement data

 

T. Debreceni, P. Szabó, G. Gy. Balázs and I. Varjasi

 

2017/04/25

Abstrac

Due to the specifically high power pulse and long duration energy requirements against lithium battery systems in electric aircrafts, the electrical design cannot be realized without sufficient modeling and simulation of such batteries considering the nonlinear behavior of cells and mission profiles. This paper presents an overview of the simulation approach and confirms the performance for using it as a design tool. The validation process of the models and simulation environment in MATLAB/Simulink includes error analysis investigating real measurement data recorded during flights of a fully electric aircraft.

Published in: Renewable Energy & Power Quality Journal (RE&PQJ, Nº. 15)
Pages: 184-188 Date of Publication: 2017/04/25
ISSN: 2172-038X Date of Current Version:
REF: 267-17 Issue Date: April 2017
DOI:10.24084/repqj15.267 Publisher: EA4EPQ

Authors and affiliations

T. Debreceni(1), P. Szabó(2), G. Gy. Balázs(2) and I. Varjasi(1)
1. Department of Automation and Applied Informatics, Budapest University of Technology and Economics, Budapest, Hungary,
2. Siemens Zrt., Budapest, Hungary

Key word

Battery simulation, battery model, validation, identification, electric vehicle, electric aircraft, mission profile.

References

[1] T. Debreceni, G. Gy. Balázs and I. Varjasi, “Mission Profile-Oriented Design of Battery Systems for Electric
Vehicles in MATLAB/Simulink®”, International Conference on Renewable Energies and Power Quality
(ICREPQ’16), Madrid, Spain, May 2016, Renewable Energy and Power Quality Journal (RE&PQJ) Conference
Proceedings.
[2] Chen, M., Rincón-Mora, G.A: “Accurate Electrical Battery Model Capable of Predicting Runtime and I–V
performance”, IEEE Transactions on energy conversion, Vol. 21 No. 2, 2006, p.504–511.
[3] Shifei Yuan, Hongjie Wu and Chengliang Yin, “State of Charge Estimation Using the Extended Kalman Filter for
Battery Management Systems Based on the ARX Battery Model”, Energies 2013, 6, 444–470.
[4] Long Lam: “A Practical Circuitbased Model for State of Health Estimation of Liion Battery Cells in Electric
Vehicles”, Master of Science Thesis, University of Technology Delft, 2011.
[5] Tibor Debreceni, Péter Szabó, Gergely György Balázs, István Varjasi, FPGA-synthesizable Electrical Battery
Cell Model for High Performance Real-time Algorithms. Periodica Polytechnica Electrical Engineering and
Computer Science, Vol. 60, No. 3, pp. 171-177, 2016. DOI:10.3311/PPee.9398
[6] He, H.; Xiong, R.; Fan, J., “Evaluation of lithium-ion battery equivalent circuit models for state of charge
estimation by an experimental approach”, Energies 2011, 4, 582–598.
[7] Rui Xiong, Xianzhi Gong, Chunting Chris Mi, Fengchun Sun: “A robust state-of-charge estimator for multiple types of lithium-ion batteries using adaptive extended Kalman filter”, Journal of Power Sources, vol. 243, 2013, 805-816.
[8] H. Rahimi-Eichi and M.-Y. Chow, "Adaptive parameter identification and State-of-Charge estimation of lithiumion
batteries," presented at the 38th Annual Conference on IEEE Industrial Electronics Society (IECON 2012), IEEE,
pp. 4012 – 4017, Montreal, QC, Canada, 2012
[9] Stefano Marsili-Libelli, “Environmental Systems Analysis with MATLAB®”, side 112., CRC Press, 2016,
ISBN 9781498706353.