Improved ANN for Estimation of Power Consumption of EV for Real-Time Battery Diagnosis-Reference-Cited by-同舟云学术

Improved ANN for Estimation of Power Consumption of EV for Real-Time Battery Diagnosis

Published:2019-05-01 Issue:3 Volume:8 Page:532-538
ISSN:2187-1094
Container-title:IEEJ Journal of Industry Applications
language:en
Short-container-title:IEEJ Journal IA

Author:

Bezha Minella¹,Nagaoka Naoto¹

Affiliation:

1. Doshisha University, Graduate School of Science and Engineering, Power System Analysis Laboratory

Publisher

Institute of Electrical Engineers of Japan (IEE Japan)

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Mechanical Engineering,Energy Engineering and Power Technology,Automotive Engineering

Link

https://www.jstage.jst.go.jp/article/ieejjia/8/3/8_532/_pdf

Reference13 articles.

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2. (2) M. Mastali, J. Vazques-Arenas, R. Fraser, M. Fowler, S. Afshar, and M. Stevens: “Battery state of the charge estimation using Kalman filtering”, Journal of Power Sources 239, pp. 294-307 (2013)

3. (3) H. He, R. Xiong, and J. Fan: “Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach”, Journal of Energies, Vol. 4, pp. 582-598 (2011)

4. (4) C. Fleischer, W. Waag, H.-M. Heyn, and D. U. Sauer: “On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models part 2. Parameter and state estimation”, Journal of Power Sources, Vol. 262, pp. 457-482 (2014)

5. (5) N. Nagaoka: “A numerical model of Lithium-ion battery for a life estimation”, Power Engineering Conference UPEC 2013 48^th International Universities (2014)

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