@inproceedings{4424153269774daf85bc975effa3939a,
title = "Artificial intelligence approach to SoC estimation for smart BMS",
abstract = "One of the most important and indispensable parameters of a Battery Management Systems (BMS) is accurate estimates of the State of Charge (SoC) of the battery. It can prevent battery from damage or premature aging by avoiding over charge/discharge. Due to the limited capacity of a battery, advanced methods must be used to estimate precisely the SoC in order to keep battery safely being charged and discharged at a suitable level and to prolong its life cycle. In this paper, we review several effective approaches: Coulomb counting, Open Circuit Voltage (OCV) and Kalman Filter method for performing the SoC estimation; then we propose Artificial Intelligence (AI) approach that can be efficiently used to precisely determine the SoC estimation for the smart battery management system as presented in [1]. By using our proposed approach, a more accurate SoC measurement will be obtained for the smart battery management system.",
keywords = "Artificial Intelligence (AI), Battery Management Systems (BMS), State of Charge (SoC)",
author = "Man, {K. L.} and C. Chen and Ting, {T. O.} and T. Krilavi{\v c}ius and J. Chang and Poon, {S. H.}",
note = "Copyright: Copyright 2015 Elsevier B.V., All rights reserved.; 7th International Conference on Electrical and Control Technologies, ECT 2012 ; Conference date: 03-05-2012 Through 04-05-2012",
year = "2012",
language = "English",
series = "7th International Conference on Electrical and Control Technologies, ECT 2012",
publisher = "Kaunas University of Technology",
pages = "21--24",
editor = "M. Azubalis and A. Navickas and A. Virbalis and V. Galvanauskas and K. Brazauskas and A. Sauhats and A. Jonaitis",
booktitle = "7th International Conference on Electrical and Control Technologies, ECT 2012",
address = "Lithuania",
}