Title :
A battery management system for a small microgrid system
Author :
Mangunkusumo, K.G.H. ; Lian, K.L. ; Aditya, P. ; Chang, Y.-R. ; Lee, Y.D. ; Ho, Y.H.
Author_Institution :
Dept. Electr. Eng., Nat. Taiwan Univ. of Sci. & Technol., Taipei, Taiwan
Abstract :
Microgrid systems, electric vehicles and portable devices need batteries as storage devices and power sources. Therefore, battery management system (BMS) is critical for maintaining optimum battery performance. In this paper, a BMS designed for a battery system of a small microgrid system in Taiwan is described. To validiate the concept, a scale-down experimental battery system is tested by the proposed BMS. For charging strategies, both two-step and multi-stage charging methods are described. Regarding the state-of-charge estimation, direct open circuit voltage (OCV), coulometric, OCV prediction, and neural network (NN) estimation methods are delineated and compared. The results indicate that NN method performs the best for predicting the state of charge.
Keywords :
battery management systems; distributed power generation; estimation theory; neural nets; BMS; NN estimation methods; OCV prediction; battery management system; direct open circuit voltage; electric vehicles; multi-stage charging methods; neural network estimation methods; optimum battery performance; portable devices; scale-down experimental battery system; small microgrid system; state-of-charge estimation; two-step charging methods; Artificial neural networks; Batteries; Battery charge measurement; Digital signal processing; Estimation; Microgrids; System-on-chip; Battery management system; State-of-Charge estimation; charge-discharge strategy;
Conference_Titel :
Intelligent Green Building and Smart Grid (IGBSG), 2014 International Conference on
Conference_Location :
Taipei
DOI :
10.1109/IGBSG.2014.6835277
