DOI QR코드

DOI QR Code

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

블루투스 기반 리튬인산철 배터리팩을 위한 BMS 모듈 알고리즘 개발에 관한 연구

  • 김종민 (동신대학교 컴퓨터학과) ;
  • 류갑상 (동신대학교 컴퓨터학과)
  • Received : 2021.01.18
  • Accepted : 2021.04.20
  • Published : 2021.04.28

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.

현재 자동차를 포함한 에너지 저장장치 제품에는 리튬 이온 배터리가 주로 사용되고 있으며, 이를 과충전하거나, 고온 상황에 방치하는 잘못된 배터리 관리 상황 발생시 폭발 등 위험한 상황에 노출될 수 있으며, 과방전 시 배터리 불능 상황을 야기한다. 이로 인해 배터리 상태를 관리해주는 시스템이 필요하며 배터리 관리 시스템은 배터리 상태를 정확하게 인지하고 각 셀의 전압을 일정하게 유지하여 최적의 배터리 효율을 얻는 데 목적이 있다. 본 논문에서는 일반적 리튬이온배터리에 비해 고안전성을 갖는 리튬인산철 배터리팩과 이를 관리하기 위해 Matlab Simulink 기반의 시뮬레이션을 사용하여 셀 특성을 확인할 수 있는 RC등가회로 모델을 이용한 분석방법을 제시하고, 저전력 및 상호통신간섭이 적은 블루투스 기반 BMS 모듈의 알고리즘을 개발하였다.

Keywords

References

  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
  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
  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
  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
  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
  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
  7. 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
  8. 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
  9. J. K. Park. (2012). Principles and applications of lithium secondary batteries, Wiley-VCH.
  10. B. Scrosati. (1992). Lithium rocking chair batteries: an old concept?. Journal of The Electrochemical Society, 139(10), 2776-2781. DOI : 10.1149/1.2068978
  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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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