DOI QR코드

DOI QR Code

A SOC Coefficient Factor Calibration Method to improve accuracy Of The Lithium Battery Equivalence Model

리튬 배터리 등가모델의 정확도 개선을 위한 SOC 계수 보정법

  • Received : 2016.12.19
  • Accepted : 2017.03.20
  • Published : 2017.04.25

Abstract

This paper proposes a battery model coefficient correction method for improving the accuracy of existing lithium battery equivalent models. BMS(battery management system) has been researched and developed to minimize shortening of battery life by keeping SOC(state of charge) and state of charge of lithium battery used in various industrial fields such as EV. However, the cell balancing operation based on the battery cell voltage can not follow the SOC change due to the internal resistance and the capacitor. Various battery equivalent models have been studied for estimation of battery SOC according to the internal resistance of the battery and capacitors. However, it is difficult to apply the same to all the batteries, and it tis difficult to estimate the battery state in the transient state. The existing battery electrical equivalent model study simulates charging and discharging dynamic characteristics of one kind of battery with error rate of 5~10% and it is not suitable to apply to actual battery having different electric characteristics. Therefore, this paper proposes a battery model coefficient correction algorithm that is suitable for real battery operating environments with different models and capacities, and can simulate dynamic characteristics with an error rate of less than 5%. To verify proposed battery model coefficient calibration method, a lithium battery of 3.7V rated voltage, 280 mAh, 1600 mAh capacity used, and a two stage RC tank model was used as an electrical equivalent model of a lithium battery. The battery charge/discharge test and model verification were performed using four C-rate of 0.25C, 0.5C, 0.75C, and 1C. The proposed battery model coefficient correction algorithm was applied to two battery models, The error rate of the discharge characteristics and the transient state characteristics is 2.13% at the maximum.

본 논문은 기존의 리튬 배터리(lithium battery) 등가모델의 정확도 개선을 위한 배터리 모델 계수 보정기법을 제안한다. 전기자동차 등 다양한 산업분야에 사용되는 리튬 배터리의 배터리 셀간 잔존용량(SOC, state of charge) 동일하게 유지하여 배터리 수명의 단축을 최소화하기 위해 BMS(battery management system)가 연구 개발 되었지만, 배터리 셀 전압 기반의 셀 밸런싱(cell balancing) 동작으로 내부저항 및 커패시터에 따른 SOC 변화를 따라가지 못한다. 배터리 내부저항 및 커패시터에 따른 배터리 SOC 추정을 위해 다양한 배터리 등가모델이 연구되었지만, 모든 배터리에 동일하게 적용하는 것은 한계가 있으며 특히 과도상태의 배터리 상태 추정이 어렵다. 기존의 배터리 전기적 등가모델 연구는 1종의 배터리를 대상으로 5~10% 오차율로 충 방전 동적특성을 모사하며 서로 다른 전기적 특성을 갖는 실제 배터리에 적용이 부적합하다. 따라서 본 논문에서는 모델 및 용량이 다른 실제 배터리 운용환경에 적합하며 오차율 5%이하의 동적특성 모사가 가능한 배터리 모델 계수 보정 알고리즘을 제안한다. 제안하는 배터리 모델 계수 보정법 검증을 위해 3.7 V 정격전압, 280 mAh, 1600 mAh 용량의 리튬 배터리를 사용하였으며, 리튬 배터리의 전기적 등가 모델로 2단 RC Tank 모델을 사용하였다. 또한 0.25C, 0.5C, 0.75C, 1C 4가지 C-rate를 사용하여 배터리 충 방전 실험 및 모델검증을 진행하였으며 제안하는 배터리 모델 계수 보정 알고리즘을 통해 구현한 두 종류의 배터리 모델의 배터리 충 방전 특성 및 과도상태 특성의 오차율은 최대 2.13%이다.

Keywords

References

  1. Mann Cho, Do-Baek Nah, Sang Chul Kil, and Sang oo Kim "Li-Ion Traction Batteries for All-Electric Vehicle," Jounal of Energy Engineering, vol. 20, no. 2, pp. 109-122, 2011. https://doi.org/10.5855/ENERGY.2011.20.2.109
  2. Mesbahi T, Khenfri F "Dynamical modeling of Li-ion batteries for electric vehicle applications based on hybrid Particle Swarm-Nelder-Mead (PSO-NM) optimization algorithm," Electric power systems research v.131, pp. 195-204 2016. https://doi.org/10.1016/j.epsr.2015.10.018
  3. S. W. Moore and P. J. Schneider, "A review of cell equalization methods for lithium ion and lithium polymer battery systems," in Proc. SAE World Congr., 2001
  4. M. Daowd, N. Omar, P. V. D. Bossche, J. V. Mierlo, "A review of Passive and Active Battery Balancing Based on MATLAB/SIMULINK," International Review of Electrical Engineering, 2011
  5. M. Chen, and G. A. Rincon-Mora, "Accurate Electrical Battery Model Capable of Predicting Runtime and I-V Performance," IEEE Tran. on Energy Conversion, vol. 21, no. 2, June 2006.
  6. O. Erdinc, B. Vural and M. Uzunoglu, "A dynamic lithium-ion battery model considering the effects of temperature and capacity fading," IEEE International Conference on Clean Electrical Power, pp. 383-386, June 2009.
  7. C. Sinkaram, K. Rajakumar, V. Asivadam, "eling Battery Management System Using The Lithium -Ion Battery," IEEE International Conference on Computing and Engineering, pp. 23-25 Nov. 2012.
  8. C. H. Lin, H. W. Huang, and K. H. Chen, "Built-in Resistance Compensation (BRC) Technique for Fas Charging Li-Ion Battery Charger," IEEE Conference on Custom Intergrated Circuits, 2008.