This paper is concerned with the State-Of-Charge (SOC) estimation for lithium-ion batteries by using an equivalent circuit model and the extended Kalman filtering (EKF) technique. The physical parameters in the equivalent circuit are dependent on both the temperature and the SOC of the battery. We propose a new method for improving the estimation accuracy based on a parameter-dependent state-state space model. To be more specific, we derive a parameter-dependent state-space model by viewing these parameters as time-varying parameters，and then apply the extended Kalman filter to estimate the SOC. Experimental results demonstrate the effectiveness of the proposed method.

Recent globalization in industries has increased the number of product failures and troubles caused by using them in unexpected ways. To avoid such troubles, it is necessary to verify whether the design plan can fulfill required functions when the product is utilized in various ways. From this point of view, the authors proposed a functional verification framework considering ways of use based on Petri net modeling and analysis of behavior of the product. However, it cannot be applied to products with sensors, since sensor faults could not be dealt with by the modeling method. This paper describes enhancement of the framework considering this problem. In the behavior modeling,some Petri net elements were introduced for representing difference between the information obtained by the sensors and the real information. This enabled the behavior model to represent sensor faults. In addition, another Petri net element that stands for a state in which the product should not be was also introduced. This made it possible to judge automatically whether an irregular phenomenon on the integrated model of behavior and physical phenomena results in a functional trouble. Application of the proposed method to an example of home cleaning robot proved its potential.

In this paper, we clarify theoretical aspects of the representative non-Gaussian filters: the ensemble Kalman filter (EnKF) and the particle filter (PF). We first show that the EnKF is a realization algorithm of the linear optimal filter for nonlinear problems. We also show that under the Gaussian assumption for the predicted state, the EnKF provides a realization algorithm of the Gaussian filter. We next propose the multiple distribution estimation approach which is a novel framework for designing non-Gaussian filters and show that the PFs are special cases. We then propose a new PF algorithm to address the particle impoverishment problem inherent in the standard PF algorithms. We also show that by applying the proposed algorithm, we can improve the filtering accuracy of the Gaussian particle filter. We finally confirm the performance of each filter using two benchmark simulation models.