In order to further improve the accuracy of electric bus energy consumption estimation and reduce the complexity of using data, the paper proposes a new method for estimating electric bus energy consumption based on a deep learning approach with a data-driven model. The method can estimate the single-trip energy consumption of an electric bus by employing CNN (convolutional neural network) to time series prediction, which takes into account easily accessible trip data of electric buses, including initial SOC (state of charge), average speed, average temperature. First, we need to convert the raw data into a trip dataset by preprocessing the collected real-world trip data of an electric bus. Then, the single trip of the bus from the original station to the terminal station is considered the basic unit for energy consumption estimation, and the trip data are processed in a quasi-time series. Following that, the trip data were modified so that the subsequent convolutional operations more closely matched the interactions between adjacent trips, and a time series prediction method based on CNN was used instead of the regression analysis methods used in traditional data-driven models. Finally, single-trip operation energy consumption estimation of electric buses is achieved with time series prediction based on CNN, and this method is compared and analysed with the LSTM (long-short term memory) time series prediction method and multivariate nonlinear regression prediction methods in traditional data-driven models. The results show that the energy consumption estimation model for electric buses developed in this paper has a higher prediction accuracy, which can improve by 3.68 percent over the traditional multivariate nonlinear regression prediction method and by 1.32 percent over the LSTM time series prediction method.
Induced ventilation system with the advantages of simple system and convenient installation has been widely used in China's underground garage ventilation design and showed an increasing trend. In this paper, the numerical simulation is done to the different air exhaust way of induction ventilation system of an underground garage. The two air exhaust ways are upper exhaust and upper 1/3 and lower 2/3. By analyzing airflow organization and pollutant concentration of the two exhaust ways, we can derive that upper 1/3 and lower 2/3 air exhaust way is conducive to the discharge of pollutants.
In order to identify the scope of active traffic control regions and improve the effect of active traffic control, this paper proposes a dynamic partitioning method of area boundaries based on benchmark intersections, taking into account the saturation, homogeneity, and correlation of intersections in the region. First, a boundary indicator correlation model was established. Next, benchmark intersections were selected based on evaluation indicators, such as traffic speed and queue length. Then, the boundary of the control region is initially defined based on the selected reference intersection, through a combination of the improved Newman algorithm. Subsequently, a spectral clustering algorithm is used to obtain the boundaries of the optimal active control subregions. Finally, a city road network is used as the study object for analysis and verification under the premise of implementing active traffic control. The results show that compared with the intersection clustering algorithm method and the boundary control subdivision method, the control effect indicators, such as the average delay and the average number of stops, have a great optimization improvement. Thus, the proposed method of regional borders combines the actual traffic flow characteristics efficiently to make a more accurate real-time dynamic division of the road network sub-areas.
The characteristics of electric buses make it difficult to estimate the energy consumption and mean that they are prone to battery loss; as such, fuel bus scheduling methods are no longer fully applicable. In current studies, the influence of these factors is ignored. This paper proposes an electric bus scheduling optimization model based on energy consumption and battery loss. Firstly, the LSTM (long short-term memory) is used to estimate trip energy consumption. Subsequently, these results are combined with the optimization objectives of minimizing the fleet size and battery loss amount. Limitations on the buses’ number, travel time, battery safety thresholds, remaining charge, and total charge are also considered. By controlling the different battery charge and discharge thresholds to minimize battery losses, the goal of sustainability is achieved. NSGA-II (non-dominated sorting genetic algorithm-II) is used to solve the model. The corresponding scheduling and charging scheme are determined. Electric bus route A is taken to validate the predictions. The results show that the annual fleet battery loss value decreases as the fleet size increases. The company has the lowest annual operating cost when the battery charge and discharge thresholds are set to [25%, 85%]. Optimizing the scheduling and charging scheme for electric bus can effectively reduce the operating cost.
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