Abstract
This paper presents the implementation of a particlefiltering- based prognostic framework that allows estimating the state-of-charge (SOC) and predicting the discharge time of energy storage devices (more specifically lithium-ion batteries). The proposed approach uses an empirical statespace model inspired in the battery phenomenology and particle-filtering to study the evolution of the SOC in time; adapting the value of unknown model parameters during the filtering stage and enabling fast convergence for the state estimates that define the initial condition for the prognosis stage. SOC prognosis is implemented using a particlefiltering- based framework that considers a statistical characterization of uncertainty for future discharge profiles.
Highlights
Energy storage devices (ESDs) play a key role in both industrial and military machinery
The resulting EOD probability density function (PDF) allows building a 95% confidence interval that provides information about when the battery would discharge if the future operating condition of the device is kept invariant. This interval has a relative high precision, it is important to mention that the bias associated to the assumed operation profile translated into overestimating the EOD time
A quick analysis of this information indicates that the proposed model for SOC estimation allows to efficiently incorporate measurement data and to generate reliable predictions paths; it must be said the high precision of the resulting PDF is only due to the fact that the uncertainty associated to the state estimates is bounded and because the uncertainty associated to the future battery use is neglected
Summary
Energy storage devices (ESDs) play a key role in both industrial and military machinery. Due to the exponential increase in the use of Li-Ion ESDs within the automotive industry, and the projected demand associated to this type of vehicles, the concept of “Battery Management Systems” – BMS, (Pattipati et al, 2011) – started to become more a necessity than a luxury These systems have as main objective (i) to provide and maximize usage time (autonomy) that is associated to a discharge cycle, (ii) to reduce battery charging times, (iii) to maximize the number of operating cycles for the ESD, and (iv) real-time operation, adjusting to sudden changes in charge/discharge conditions. INTERNATIONAL JOURNAL OF PROGNOSTICS AND HEALTH MANAGEMENT effective) SOC prognosis approach: (i) how to model the battery, (ii) how to estimate the SOC in a nonlinear, nonobservable system, and (iii) how to predict the impact of future discharge profiles in the evolution of SOC in time.
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