Dynamic data-driven identification of battery state-of-charge via symbolic analysis of input–output pairs. (1st October 2015)

• Record Type: Journal Article
• Authors: Li, Yue; Chattopadhyay, Pritthi; Ray, Asok
• Is Part Of: Applied energy. Volume 155(2015:Oct. 01)
• Page Start: 778
• Page End: 790
• Publication Date: 2015-10-01
• Subjects: State-of-charge -- Symbolic dynamics -- Pattern classification; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2015.06.040
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Symbolic Time Series Analysis (STSA) has been used for low-complexity feature extraction. Discrete wavelet transformation (DWT) has been used for data segmentation. Algorithms have been validated on experimental data of pairs of current and voltage data from a lead-acid battery. Abstract: This paper presents a dynamic data-driven method of pattern classification for identification of the state-of-charge (SOC) parameter in battery systems for diverse applications (e.g., plug-in electric vehicles and hybrid locomotives). The underlying theory is built upon the concept of symbolic dynamics, which represents the behavior of battery system dynamics at different levels of SOC as probabilistic finite state automata (PFSA). In the proposed method, (finite-length) blocks of battery data are selected via wavelet-based segmentation from the time series of synchronized input–output (i.e., current–voltage) pairs in the respective two-dimensional space. Then, symbol strings are generated from the segmented time series pairs in the sense of maximum entropy partitioning and a special class of PFSA, called the D-Markov machine, is constructed to extract the features of the battery system dynamics for pattern classification. To deal with the uncertainties due to the (finite-length) approximation of symbol sequences, combinations of (a priori) Dirichlet and (a posteriori) multinomial distributions are respectively adopted in the training and testing phases of pattern classification. The proposed concept of pattern classification has been validated on (approximately periodic) experimental data that have been acquired from a commercial-scale lead-acid battery.