Lithium-ion batteries are the primary power source in electric vehicles, and the prognosis of their remaining useful life is vital for ensuring the safety, stability, and long lifetime of electric vehicles. Accurately establishing a mechanism model of a vehicle lithium-ion battery involves a complex electrochemical process. Remaining useful life (RUL) prognostics based on data-driven methods has become a focus of research. Current research on data-driven methodologies is summarized in this paper. By analyzing the problems of vehicle lithium-ion batteries in practical applications, the problems that need to be solved in the future are identified.

Fuzzy Cognitive Map (FCM) offers many advantages such intuitive knowledge representation and fast numerical reasoning ability, etc. It suits modeling and decision-making of dynamic systems. With the aims to effectively help to analyze and control system sustainable evolution, the paper defines the steady states of fixed point and limited cycle of a FCM modeling system. Accordingly, the rules of steady states of the FCM model and the factors influencing the steady states are presented and proved. The Three-Rivers represents a system including population, ecological environment, social development and their relationships. Based on the relationships, the Three-Rivers ecosystem is modeled by FCM and the Three-Rivers ecosystemsustainable evolutionis analyzed bythe rules of the steady states of FCM.

The MOSFET is an important power electronic transistor widely used in electrical systems. Its reliability has an effect on the performance of systems. In this paper, the failure models and mechanisms of MOSFETs are briefly analyzed. The on-resistanceRonis the key failure precursor parameter representing the degree of degradation. Based on the experimental data, a nonlinear dual-exponential degradation model for MOSFETs is obtained. Then, we present an approach for MOSFET degradation state prediction using a strong tract filter based on the obtained degradation model. Lastly, the proposed algorithm is shown to perform effectively on experimental data. Thus, it can provide early warning and enhance the reliability of electrical systems.

Haze-fog has seriously hindered the sustainable development of the ecological environment and caused great harm to the physical and mental health of residents in China. Therefore, it is important to probe the formation of haze-fog for its early warning and prevention. The formation of haze-fog is, in fact, a fuzzy nonlinear process. The formation of haze-fog is such a complex process that it is difficult to simulate its dynamic evolution using traditional methods, mainly because of the lack of their consideration of the nonlinear relationships. It is, therefore, essential to explore new perspectives on the formation of haze-fog. In this work, previous research on haze-fog formation is summarized first. Second, a new perspective is proposed on the application of fuzzy cognitive map to the formation of haze-fog. Third, a data mining method based on the genetic algorithm is used to discover the causality values of a fuzzy cognitive map (FCM) for hazefog formation. Finally, simulation results are obtained through an experiment using the fuzzy cognitive map and its data mining method for the formation of haze-fog. The validity of this approach is determined by definition of a simple rule and the Kappa values. Thus, this research not only provides a new idea using FCM modeling the formation of haze-fog, but also uses an effective method of FCM for solving the nonlinear dynamics of the haze-fog formation.

In order to model multi-dimensions and multi-granularities oriented complex systems, this paper firstly proposes a kind of multi-relational Fuzzy Cognitive Map (FCM) to simulate the multi-relational system and its auto construct algorithm integrating Nonlinear Hebbian Learning (NHL) and Real Code Genetic Algorithm (RCGA). The multi-relational FCM fits to model the complex system with multi-dimensions and multi-granularities. The auto construct algorithm can learn the multi-relational FCM from multi-relational data resources to eliminate human intervention. The Multi-Relational Data Mining (MRDM) algorithm integrates multi-instance oriented NHL and RCGA of FCM. NHL is extended to mine the causal relationships between coarse-granularity concept and its fined-granularity concepts driven by multi-instances in the multi-relational system. RCGA is used to establish high-quality high-level FCM driven by data. The multi-relational FCM and the integrating algorithm have been applied in complex system of Mutagenesis. The experiment demonstrates not only that they get better classification accuracy, but it also shows the causal relationships among the concepts of the system.

Switching Mode Power Supply (SMPS) has been widely applied in aeronautics, nuclear power, high-speed railways, and other areas related to national strategy and security. The degradation of MOSFET occupies a dominant position in the key factors affecting the reliability of SMPS. MOSFETs are used as low-voltage switches to regulate the DC voltage in SMPS. The studies have shown that die-attach degradation leads to an increase in on-state resistance due to its dependence on junction temperature. On-state resistance is the key indicator of the health of MOSFETs. In this paper, an online real-time method is presented for predicting the degradation of MOSFETs. First, the relationship between an oscillator signal of source and on-state resistance is introduced. Because oscillator signals change when they age, a feature is proposed to capture these changes and use them as indicators of the state of health of MOSFETs. A platform for testing characterizations is then established to monitor oscillator signals of source. Changes in oscillator signal measurement were observed with aged on-state resistance as a result of die-attach degradation. The experimental results demonstrate that the method is efficient. This study will enable a method to predict the failure of MOSFETs to be developed.