This paper aims to improve the rapidity and accuracy of the State of Health (SOH) assessment for lithium-ion battery. By integrating the Unscented Kalman Filter (UKF) and Improved Unscented Particle Filter (IUPF) algorithm, SOH can be effectively evaluated. The UKF algorithm is used to estimate the state of charge (SOC), the IUPF algorithm is employed to identify the ohmic internal resistance. The novelty of the proposed strategy relies on the 4-dimensional IUPF filter that is split into a 3-dimensional UKF filter and a 1-dimensional IUPF filter. Experimental results demonstrate that more accuracy and a faster rate of SOH estimation can be achieved via the UKFIUPF algorithm compared to the IUPF approach.

Anomaly detection is an important challenge in wireless sensor networks for some applications, which require efficient, accurate, and timely data analysis to facilitate critical decision making and situation awareness. Support vector data description is well applied to anomaly detection using a very attractive kernel method. However, it has a high computational complexity since the standard version of support vector data description needs to solve quadratic programming problem. In this article, an improved method on the basis of support vector data description is proposed, which reduces the computational complexity and is used for anomaly detection in energy-constraint wireless sensor networks. The main idea is to improve the computational complexity from the training stage and the decision-making stage. First, the strategy of training sample reduction is used to cut back the number of samples and then the sequential minimal optimization algorithm based on the second-order approximation is implemented on the sample set to achieve the goal of reducing the training time. Second, through the analysis of the decision function, the pre-image in the original space corresponding to the center of hyper-sphere in kernel feature space can be obtained. The decision complexity is reduced from O(l) to O(1) using the pre-image. Eventually, the experimental results on several benchmark datasets and real wireless sensor networks datasets demonstrate that the proposed method can not only guarantee detection accuracy but also reduce time complexity.

In recent years, a large number of wind power has been applied in the micro-grid (MG). Influenced by randomness characteristics of wind speed, the uncertainty in the power output of wind turbines imposes some safety and stability problems on the optimal energy management in MG. To address this problem, an expert energy management system (EEMS) considering wind power probability is developed in this study for optimal dispatching of a typical grid-connected MG. The EEMS composes of wind power probabilistic forecasting module, multi-objective optimization module and energy storage system (ESS) module. In the wind power forecasting module, wind power probabilistic forecasting based on complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and Gaussian process regression (GPR) is proposed in this study. To improve the forecasting results, CEEMDAN, an effective signal processing method, is employed to decompose the wind power data, then, the decomposed subseries are utilized as the inputs of GPR for probabilistic forecasting. A two-step solution methodology combining an efficient and effective improved multi-objective bat algorithm (IMOBA) with fuzzy set theory (FST) is put forward to solve the optimal dispatching problems. In the first step, IMOBA is developed to optimize the energy dispatching of EEMS by minimizing both economic cost and pollutant emissions simultaneously, and obtain a well-distributed set of Pareto optimal front (POF), then, FST is employed to identify the best compromise solutions from POF. Six operational scenarios of a typical grid-connected MG based one-POF-one-day and one-POF-one-hour dispatching schemes are constructed to investigate the effectiveness of the proposed strategy and provide more flexibility for decision makers. The results illustrate that EEMS can effectively schedule power generation and energy storage by considering economic cost and pollutant emission objectives simultaneously.

Indoor location-based-service is emerging as the crucial application of internet-of-things, which promotes the advance of relevant technology in indoor scenario. Several positioning algorithms are proposed for different indoor configurations in recent years. The fingerprint based indoor positioning algorithm has drawn much attention because of the good positioning performance without additional hardware. However, the false fingerprint matching frequently incurs due to the complexity of indoor positioning environment and affects the positioning accuracy. In this paper, a principal component analysis (PCA) assisted indoor positioning algorithm based on the adaptive hierarchical clustering algorithm (PAHC) is proposed, which can improve the positioning accuracy through aggregating the RPs and conducting the cluster-based-principal-component-analysis (C-PCA) features extraction. More specifically, a clustering termination method is proposed to obtain reasonable RPs clusters adaptively according to the pre-set RPs. And a two-stage fingerprint matching algorithm is proposed based on the C-PCA to further increasing the difference between similar RPs and thus improving the positioning accuracy. To verify the proposed algorithm, an indoor wireless system is established in the practical indoor scenario. Experiment results indicate that the proposed PAHC algorithm can increase the positioning accuracy by 9.3% compared with the conventional fingerprint algorithm.

In order to achieve zero voltage switch (ZVS) and zero current switch (ZCS) of half-bridge LLC resonant converter, an optimal design method of the converter was presented. In this work, a detailed optimization procedure is proposed to optimize LLC resonant converter parameters. The main circuit steady state equivalent circuit model was established based on fundamental harmonic approximation method (FHA). By means of the circuit normalization of DC gain, resonant current and zero-voltage-switching condition was theoretically analyzed, and the converter key parameters including quality factor, magnetic inductor, resonant inductor and resonant capacitor were optimized. Finally, a prototype 200W LLC converter was designed and built up using the optimization method. The experimental results verify that the proposed optimization procedure is effective.