Abstract
In this paper, one unscented Kalman filter with adjustable scaling parameters is proposed to estimate the state of charge (SOC) for lithium-ion batteries, as SOC is most important in monitoring the latter battery management system. After the equivalent circuit model is applied to describe the lithium-ion battery charging and discharging properties, a state space equation is constructed to regard SOC as its first state variable. Based on this state space model about SOC, one state estimation problem corresponding to the nonlinear system is established. In implementing the unscented Kalman filter, state estimation is influenced by the scaling parameter. Then, one criterion function is constructed to choose the scaling parameter adaptively by minimizing this criterion function. To extend one single unscented Kalman filter with adjustable scaling parameters to multiple module estimation, one improved unscented Kalman filter is advised based on iterative multiple models. Generally, the main contributions of this paper consist in two folds: one is to introduce a selection strategy for the scaling parameter adaptively, and the other is to combine iterative multiple models and a single unscented Kalman filter with adjustable scaling parameters. Finally, two simulation examples confirm that our unscented Kalman filter with adjustable scaling parameters and its improved iterative form are better than the classical Kalman filter; i.e., our obtained SOC estimation error converges to zero.
Highlights
Lithium-ion battery is the leading energy storage technology for many research fields, such as electric vehicle, modern electric grids, transformation, etc. e main features of lithium-ion batteries include energy density, a long time, and a lower self-discharge rate, so many research studies on these main features of lithium-ion batteries are carried out in recent years from their own different points of view
Kalman filter was first proposed to estimate the state of linear systems [12], and in order to apply it into nonlinear systems, the extended Kalman filter and unscented Kalman filter were developed [11]
Because the state space equation, constructed by physical principle of the lithium-ion battery, coincides with a nonlinear system, one unscented Kalman filter is proposed to study the problem of state of charge (SOC) estimation for a nonlinear system at a series of points, where this nonlinear system corresponds to our state space equation about SOC
Summary
Lithium-ion battery is the leading energy storage technology for many research fields, such as electric vehicle, modern electric grids, transformation, etc. e main features of lithium-ion batteries include energy density, a long time, and a lower self-discharge rate, so many research studies on these main features of lithium-ion batteries are carried out in recent years from their own different points of view. Because the state space equation, constructed by physical principle of the lithium-ion battery, coincides with a nonlinear system, one unscented Kalman filter is proposed to study the problem of SOC estimation for a nonlinear system at a series of points, where this nonlinear system corresponds to our state space equation about SOC When implementing this unscented Kalman filter, the accuracy of SOC estimation is influenced by one designed scaling parameter. (2) On the basis of information fusion theory, the idea of iterative multiple models is applied to implement our proposed unscented Kalman filter with adjustment scaling parameter, the weighted summation from these multiple models is set as the final state estimation, and the weights are determined by probability level. A flowchart of our proposed unscented Kalman filter with the adjustment scaling parameter and its other improved multiple models is given in Figure 1, where the yellow parts are our main contributions
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