DocumentCode :
567709
Title :
An EM approach for dynamic battery management systems
Author :
Balasingam, B. ; Pattipati, B. ; Sankavaram, C. ; Pattipati, K. ; Bar-Shalom, Y.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Connecticut, Storrs, CT, USA
fYear :
2012
fDate :
9-12 July 2012
Firstpage :
2110
Lastpage :
2117
Abstract :
In this paper we propose an expectation maximization (EM) type algorithm for online system identification and tracking of prognostic states as applied to battery management systems (BMS). The objective of BMS is to adaptively estimate state of charge (SOC) - a crucial battery state information. We find that the existing approaches in the literature need enhancement because (i) they lack battery equivalent models that accurately model the actual physical and chemical properties of the batteries, and (ii) they fail to utilize powerful state and parameter estimation techniques for system identification and tracking. It is often noticed throughout the literature that (ii) is a precursor to (i). In this paper, first we model the dynamic equivalent model of batteries as a series of m parallel RC circuits and derive the relationship between the time-varying battery states and the current, voltage output observations as a non-linear state space model. Then, we derive an expectation maximization (EM) type algorithm for identification of the so-derived statespace model and for the adaptive tracking of SOC. Finally we discuss the performance evaluation of the proposed algorithm through simulation and by testing them on experimental data obtained from Li-ion based cellphone batteries.
Keywords :
battery management systems; parameter estimation; secondary cells; BMS; EM approach; EM type algorithm; SOC; battery equivalent models; battery state information; chemical properties; dynamic battery management systems; dynamic equivalent model; expectation maximization type algorithm; lithium ion based cellphone batteries; online system identification; parameter estimation techniques; physical properties; prognostic states tracking; state of charge; time-varying battery states; Adaptation models; Batteries; Battery management systems; Equivalent circuits; Integrated circuit modeling; System-on-a-chip; Voltage measurement; Battery management system (BMS); EKF smoothing; non-linear filtering; online system identification; the expectation maximization (EM) algorithm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Fusion (FUSION), 2012 15th International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4673-0417-7
Electronic_ISBN :
978-0-9824438-4-2
Type :
conf
Filename :
6290560
Link To Document :
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