An EM approach for dynamic battery management systems

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. © 2012 ISIF (Intl Society of Information Fusi)

Online anomaly detection in big data

In this paper, the problem of online anomaly detection in multi-attributed, asynchronous data from a large number of individual devices is considered. It has become increasingly common for many services, such as video-on-demand (VOD), to have connected customers where hundreds of millions of subscribers access a cluster of content servers for online services. It is important to monitor these transactions online, in order to ensure acceptable quality of experience to the customers as well as for detecting any abnormal or undesirable activities. Our proposed anomaly detection strategy works in two phases: First we perform intermittent anomaly detection in space, using data from the entire set of devices for a short duration in time. This phase employs principal component analysis (PCA) for data reduction and captures models of normal and abnormal features. Then, these identified models are used to monitor each subscriber\u27s devices online in order to quickly detect any abnormalities. The proposed approach is demonstrated on Comcast\u27s Xfinity video streaming data