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The haze problem in cities poses a great threat to human health. Although there are many factors that cause fog and haze, the main reason is the increase of the PM2.5 concentration. Due to the complexity of the particles motion, it is difficult to use traditional methods obtain information about PM2.5 (including its sources, influencing factors, and distribution forecast). This paper presents a cellular automata (CA) model based on a multivariate regression model and several physical models to analyze the generation and diffusion of PM2.5. In Beijing for example, after the researches, the multiple regression confirmed that the major source of PM2.5 is vehicle pollution, which accounts for 39.2% of the pollution generated in Beijing. The secondary source is from the residential areas, accounting for 27.5% in the winter. Besides, 32% of the total pollution comes from nearby areas. In addition, it is also confirmed that the weather factors, such as, temperature, wind, pressure and relative humidity, and radiation are all have a great impact on PM2.5. The CA model is demonstrated to be an effective simulation and prediction method for PM2.5 since it allows for the estimation of the governance results by simulating the control scheme and predicting the concentration of PM2.5 accurately in the next 48 h. In the prediction experiment, 77.5% of prediction error is less than $20~ {\mu }\text{g} / {\text {m}}^{3}$. [ABSTRACT FROM AUTHOR]

Air quality forecasting is a crucial part of megacities control and planning. It is necessary to develop high-quality forecasting methods in real-world applications, especially the issues that affect public health. Like many real-world problems, air quality is affected by uncertainties of nonstationary characteristics that their statistical attributes frequently change over time. Type-2 fuzzy systems have the capability to handle high-order uncertainties such as sequential dependencies associated with time series. This study develops a type-2 fuzzy intelligent system for air quality forecasting. Additionally, a dynamic time warping algorithm has been applied to estimate the pattern's similarity in long time series. The proposed model has been evaluated on a real dataset that contains the one-decade information about outdoor pollutants from April 2011 to November 2020 in Tehran and Beijing. The experiments have confirmed that the proposed model has lower forecasting error than counterpart models in terms of the RMSE, MAE, and MPE for both Tehran and Beijing time-series datasets. Also, the results confirm the superiority of the proposed model with an average area under the ROC curve (AUC) of 95% with a 95% confidence interval. This is promising for the prediction of air quality and make strategic prevention decisions. [ABSTRACT FROM AUTHOR]

The state of charge (SOC) is a crucial parameter of a battery management system for Li-ion batteries. The SOC indicates the amount of charge left in the battery of electric vehicles—akin to the fuel gauge in combustion vehicles. An accurate SOC knowledge contributes largely to the longevity, performance, and reliability of the battery. However, the SOC of Li-ion batteries cannot be easily measured by any apparatus. Furthermore, the SOC can also be influenced by numerous incalculable factors such as battery chemistry, ambient environment, aging factor, etc. In this article, we propose an SOC estimation model for a Li-ion battery using an improved deep neural network (DNN) approach for electric vehicle applications. We found that a DNN with a sufficient number of hidden layers is capable of predicting the SOC of the unseen drive cycles during training. We developed a series of DNN models with a varying number of hidden layers, and its training algorithm was to investigate their respective performance when evaluated on different drive cycles. We observe that the increasing number of hidden layers in the DNN (up to four hidden layers) decreases the error rate and improves SOC estimation. An additional increase in the number of hidden layers beyond that increases the error rate. In this study, we show that a four-hidden-layer DNN trained on Dynamic Stress Test drive cycle is capable of predicting SOC values unexpectedly well of other unseen drive cycles such as Federal Urban Driving Schedule, Beijing Dynamic Stress Test, and Supplemental Federal Test Procedure, respectively. [ABSTRACT FROM AUTHOR]