Browse > Article
http://dx.doi.org/10.15207/JKCS.2021.12.4.001

A Study on Development of BMS module Algorithm for Bluetooth-based Lithium-Iron Phosphate Battery pack  

Kim, Jong-Min (Dept. of Computer Science, Dongshin University)
Ryu, Gab-Sang (Dept. of Computer Science, Dongshin University)
Publication Information
Journal of the Korea Convergence Society / v.12, no.4, 2021 , pp. 1-8 More about this Journal
Abstract
Currently, lithium-ion batteries are mainly used in energy storage equipment products including automobiles. This can be exposed to dangerous situations such as explosions in the event of incorrect battery management conditions that are overcharged or left in high temperature conditions. It also causes a situation battery cannot be used when it has been over discharged. Therefore, a system that manages the state of the battery is required. The battery management system aims to obtain optimum battery efficiency by accurately recognizing the state of the battery and keeping the voltage of each cell constant. In this paper, we develop a lithium-iron phosphate battery that has higher safety than a general lithium-ion battery. Then, in order to manage this, we try to develop the algorithm of the BMS module based on the Bluetooth communication using the MATLAB-SIMULINK.
Keywords
Lithium-ion battery; Lithium-iron phosphate; Battery management system; Electric vehicle; Energy storage system;
Citations & Related Records
연도 인용수 순위
  • Reference
1 M. M. Thackeray, C. Wolverton & E. D. Isaacs. (2012). Electrical energy storage for transportation-approaching the limits of, and going beyond, lithium-ion batteries. Energy & Environmental Science, 5(7), 7854-7863. DOI : 10.1039/C2EE21892E   DOI
2 T. H. Kim, J. S. Park, S. K. Chang, S. Choi, J. H. Ryu & H. K. Song. (2012). The current move of lithium ion batteries towards the next phase. Advanced Energy Materials, 2(7), 860-872. DOI : 10.1002/aenm.201200028   DOI
3 B. Scrosati & J. Garche. (2010). Lithium batteries: Status, prospects and future. Journal of power sources, 195(9), 2419-2430. DOI : 10.1016/j.jpowsour.2009.11.048   DOI
4 W. Zhang, W. Shi & Z. Ma. (2015). Adaptive unscented Kalman filter based state of energy and power capability estimation approach for lithium-ion battery. Journal of Power Sources, 289, 50-62. DOI : 10.1016/j.jpowsour.2015.04.148   DOI
5 L. Lu, X. Han, J. Li, J. Hua & M. Ouyang. (2013). A review on the key issues for lithium-ion battery management in electric vehicles. Journal of power sources, 226, 272-288. DOI : 10.1016/j.jpowsour.2012.10.060   DOI
6 L. W. Juang, P. J. Kollmeyer, T. M. Jahns & R. D. Lorenz (2012). Implementation of online battery state-of-power and state-of-function estimation in electric vehicle applications. 2012 IEEE Energy Conversion Congress and Exposition(ECCE), 1819-1826. DOI : 10.1109/ECCE.2012.6342591   DOI
7 B. Scrosati. (1992). Lithium rocking chair batteries: an old concept?. Journal of The Electrochemical Society, 139(10), 2776-2781. DOI : 10.1149/1.2068978   DOI
8 L. Wang, Y. Cheng & J. Zou. (2014). Battery available power prediction of hybrid electric vehicle based on improved Dynamic Matrix Control algorithms. Journal of power sources, 261, 337-347. DOI : 10.1016/j.jpowsour.2014.03.091   DOI
9 J. Desilvestro & O. Haas. (1990). Metal oxide cathode materials for electrochemical energy storage: a review. Journal of the Electrochemical Society, 137(1), 5C-22C. DOI : 10.1149/1.2086438   DOI
10 J. K. Park. (2012). Principles and applications of lithium secondary batteries, Wiley-VCH.
11 K. M. Abraham. (1993). Directions in secondary lithium battery research and development. Electrochimica Acta, 38(9), 1233-1248. DOI : 10.1016/0013-4686(93)80054-4   DOI
12 J. Lee, J. H. Ahn & B. K. Lee. (2017). A novel li-ion battery pack modeling considerging single cell information and capacity variation. In 2017 IEEE Energy Conversion Congress and Exposition(ECCE), 5242-5247. DOI : 10.1109/ECCE.2017.8096880   DOI
13 G. L. Plett. (2004). High-performance battery-pack power estimation using a dynamic cell model. IEEE Transactions on vehicular technology, 53(5), 1586-1593. DOI : 10.1109/TVT.2004.832408   DOI
14 T. Kim & W. Qiao. (2011). A hybrid battery model capable of capturing dynamic circuit characteristics and nonlinear capacity effects. IEEE Transactions on Energy Conversion, 26(4), 1172-1180. DOI : 10.1109/TEC.2011.2167014   DOI
15 J. Li & M. S. Mazzola. (2013). Accurate battery pack modeling for automotive applications. Journal of Power Sources, 237, 215-228. DOI : 10.1016/j.jpowsour.2013.03.009   DOI
16 A. P. Schmidt, M. Bitzer, A. W. Imre & L. Guzzella. (2010). Experiment-driven electrochemical modeling and systematic parameterization for a lithium-ion battery cell. Journal of Power Sources, 195(15), 5071-5080. DOI : 10.1016/j.jpowsour.2010.02.029   DOI