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Virtual Environment Modeling for Battery Management System
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 Title & Authors
Virtual Environment Modeling for Battery Management System
Piao, Chang-Hao; Yu, Qi-Fan; Duan, Chong-Xi; Su, Ling; Zhang, Yan;
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 Abstract
The offline verification of state of charge estimation, power estimation, fault diagnosis and emergency control of battery management system (BMS) is one of the key technologies in the field of electric vehicle battery system. It is difficult to test and verify the battery management system software in the early stage, especially for algorithms such as system state estimation, emergency control and so on. This article carried out the virtual environment modeling for verification of battery management system. According to the input/output parameters of battery management system, virtual environment is determined to run the battery management system. With the integration of the developed BMS model and the external model, the virtual environment model has been established for battery management system in the vehicle`s working environment. Through the virtual environment model, the effectiveness of software algorithm of BMS was verified, such as battery state parameters estimation, power estimation, fault diagnosis, charge and discharge management, etc.
 Keywords
Battery management system;Virtual environment;Electric vehicle;Simulation;
 Language
English
 Cited by
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Analysis of Real-Time Estimation Method Based on Hidden Markov Models for Battery System States of Health, Journal of Power Electronics, 2016, 16, 1, 217  crossref(new windwow)
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Lithium-Ion Battery Cell-Balancing Algorithm for Battery Management System Based on Real-Time Outlier Detection, Mathematical Problems in Engineering, 2015, 2015, 1  crossref(new windwow)
3.
Fault detection of the connection of lithium-ion power batteries based on entropy for electric vehicles, Journal of Power Sources, 2015, 293, 548  crossref(new windwow)
 References
1.
Hu Bei, "The development of Ni-MH battery," China Metal Bulletin, pp. 18-19, Feb. 2010.

2.
Chang Guofeng, Cheng Leitao, Xu Sichuan and Wang Lina, "Structural Optimization Design of Nickelhydrogen Battery Thermal Management System," Journal of Tongji University, pp. 1518-1520, Nov. 2009.

3.
Zhao Yuzhen and Wang Lihua, "Battery Management System Used for HEV," Private science and technology, pp. 40-41, May. 2010.

4.
Feng Xuyun, "Situation and Development on Hybrid Electric Vehicle Battery (Ni-MH) Management System," China Science and Technology Information, pp. 130-131, August.2008.

5.
Zhang Jianbo, Lu Languang and Li Zhe, "Key technologies and fundamental academic issues for traction battery systems," Automotive Safety and Energy, vol. 3, no. 2, pp. 87-104, 2012.

6.
Lu Juxiao, Lin Chengtao and Chen Quanshi, "Comparison study of 3 types of battery models for electrical vehicle," Power Sources, vol. 7, pp. 535-538, 2006.

7.
Fu Wenli, "Research on Ni/MH battery software simulation platform," Master Dissertation. Chongqing University of Posts and Telecommunications, 2010.

8.
Hauer K-H, "Analysis tool for fuel cell vehicle hardware and software with an application to fuel economy compariosons of alternative system designs," Ph.D Dissertation. University of California Davis, 2001.

9.
Piao Changhao, Fu Wenli and Lei Gaihui, "Online Parameter Estimation of the Ni-MH Batteries Based on Statistical Methods,"Energies, vol. 3, no. 2, pp. 206-215, 2009.

10.
Hu Xiao song, Li Shengbo and Peng H, "A comparative study of equivalent circuit models for Li-ion batteries," Journal of Power Sources, vol. 198, pp. 359-367, 2012. crossref(new window)

11.
Roscher M A, Bohlen O S and Sauer D U, "Reliable state estimation of multi-cell lithium-ion battery systems," IEEE Trans on Energy Conversion, vol. 26, no. 3, pp. 737-743, 2011. crossref(new window)

12.
Kimura A, Abe T, Sasaki S, "Drive force control of a parallel-series hybrid system,"JSAE Review, vol. 2, no. 3, pp. 337-341, 1999.

13.
Rahman Z, Butler K L and Ehsani M, "A comparison study between two parallel hybrid control concepts," SAE Paper, pp. 990-994, 2000.

14.
Pu Jinhuan, Yan Juanqi and Zhang Jianwu, "Research on Energy Optimal Management and Control Strategies for Hybrid Electric Vehicles," Master Dissertation. Shanghai Jiao Tong University, 2005.

15.
Wang Xiaoyuan, Zhang Jinglei and Meng Zhaowei, "Artificial neural network (ANN) model for car following simulation of microscopic traffic flow," J Shandong Univ of Tech. vol. 18, no. 4, pp. 1-6, 2004.

16.
L. Gan, X. L. Pan, "A method of moving vehicles tracking applied in traffic environment," Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), vol. 25, no. 3, pp. 408-411, 2013.

17.
X. Li, S. J. Zhang, N. S. Xu, "characteristic analysis and simulation of new-type continuously variable transmission carried in EV," Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), vol. 24, no. 1, pp. 109-113, 2012.

18.
He Dawei, "Research on the control strategies and simulation in hybrid vehicle based on CVT," Master Dissertation. Hunan University, 2008.

19.
Changhao Piao, Chongxi Duan, Yusheng Li and Sheng Lu, "Research on The Driver's Following Behavior Based on Hybrid Electric Vehicle Model," in Proceedings of IEEE ICMEME 2012 Conference, Dalian, China, Oct. 2012.

20.
Karden E, Mauracher P, Schope F, "Electrochemical Modeling of Lead/Acid Batteries under Operating Conditions of electric Vehicles," J. Electrochem. Soc, vol. 64, no. 1, pp. 175-180, 1997.

21.
Bergveld H J, Kruijt W S and Notteln P H.L, "Battery Management Systems Design by Modelling," aaKluwer Academic Publishers, Netherlands, 2002.

22.
O'Gorman C C, Ingersoll D and Jungst R G, "Artificial neural network simulation of battery performance," in Proceedings of 31st Annual Hawaii International Conference on System Sciences, Hawaii, USA, 1998.