 Dynamic data-driven and model-based recursive analysis for estimation of battery state-of-chargeYue Li, Pritthi Chattopadhyay, Sihan Xiong, Asok Ray and Christopher D. Rahn Applied Energy, 2016, vol. 184, issue C, 266-275 Abstract: This paper addresses estimation of battery state-of-charge (SOC) from the joint perspectives of dynamic data-driven and model-based recursive analysis. The proposed SOC estimation algorithm is built upon the concepts of symbolic time series analysis (STSA) and recursive Bayesian filtering (RBF) that is a generalization of the conventional Kalman filtering. A special class of Markov models, called ×D-Markov (pronounced as cross D-Markov) machine, is constructed from a symbolized time-series pair of input current and output voltage. A measurement model of SOC is developed based on the features obtained from the ×D-Markov machine. Then, a combination of this measurement model and a low-order model of the SOC process dynamics is used for construction of the RBF. The proposed algorithm of SOC estimation has been validated on (approximately periodic) experimental data of (synchronized) current-voltage time series, generated from a commercial-scale lead-acid battery system. Keywords: Battery state of charge; Dynamic data-driven application systems; Symbolic time series analysis; Recursive Bayesian filtering (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:184:y:2016:i:c:p:266-275 Ordering information: This journal article can be ordered from DOI: 10.1016/j.apenergy.2016.10.025 Access Statistics for this article Applied Energy is currently edited by J. Yan More articles in Applied Energy from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.