Battery state-of-charge and parameter estimation algorithm based on Kalman filter

This paper presents consensus-based distributed control strategies for voltage regulation and power flow control of dc microgrid (MG) clusters. In the proposed strategy, primary level of control is used to regulate the common bus voltage inside each MG locally. An SOC-based adaptive droop method is introduced for this level which determines droop coefficient automatically, thus equalizing SOC of batteries inside each MG. In the secondary level, a distributed consensus based voltage control strategy is proposed to eliminate the average voltage deviation while guaranteeing proper regulation of power flow among the MGs. Using the consensus protocol, the global information can be accurately shared in a distributed way. This allows the power flow control to be achieved at the same time as it can be accomplished only at the cost of having the voltage differences inside the system. Similarly, a consensus-based cooperative algorithm is employed at this stage to define appropriate reference for power flow between MGs according to their local SOCs. The effectiveness of proposed control scheme is verified through detailed hardware-in-the-loop (HIL) simulations.

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“…There exist several advanced methods to estimate SOC [18], [19]. Here we use ampere counting method which describes as follows…”

Section: B Adaptive Droop Controlmentioning

confidence: 99%

Distributed consensus-based control of multiple DC-microgrids clusters

Shafiee

Dragičević

Andrade

et al. 2014

IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society

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“…The dual extended Kalman filter is presented in [44,45]. Two Kalman filter are used: one to estimate the SoC and the other to estimate the battery parameters, such as the capacity.…”

Section: Kalman Filteringmentioning

confidence: 99%

“…The EKF state equations are developed based on the first-order Randles electrical model. Architecture of the dual extended Kalman filter [44,45]. The method was applied considering three classification classes for the batteries: unused, Figure 7.…”

Section: Kalman Filteringmentioning

confidence: 99%

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Battery state of health estimation: a structured review of models, methods and commercial devices

Ungurean

Cârstoiu

Micea

et al. 2016

Int. J. Energy Res.

199

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Summary Estimating the dynamic status parameters of a battery, such as its state of health (SoH) and remaining useful life (RUL), is still a very difficult and complex task. In this paper we perform a structured review of the most relevant state of the art models, algorithms and commercial devices employed in the estimation of the SoH/RUL battery performance figures, in the context of embedded applications. The models and estimation techniques are thoroughly classified and, for each taxonomy class, a presentation of the working principles is made. A comprehensive set of metrics is then introduced for the evaluation of the SoH/RUL estimation techniques from the perspective of their implementation and operation efficiency in embedded systems. These algorithms are then analyzed and discussed in a comparative manner, with concrete figures and results. The capability and the performance of the different types of off‐the‐shelf fuel gauges to estimate the battery SoH/RUL parameters are also evaluated in this paper. Copyright © 2016 John Wiley & Sons, Ltd.

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“…Kalman filter [11]- [12]- [13] Predict the battery SOC using the components of a battery model. It works in two steps prediction and measurement.…”

Section: Onlinementioning

confidence: 99%