Research focused on the real-time rescheduling of high-speed railway traffic with a quasi-moving blocking system and transition process affected by the entrance delays and disruptions determining speed limitation. A mixed-integer linear program model related to a job shop model of operations is formulated to reduce the final delay (tardiness) of trains, where three objective functions combine different manners related to traffic control and speed management. The dynamic interaction between train speed and distance headway is considered in the model. Through experiments on a real-world high-speed line in China, the solution quality of the model is assessed by the delay distribution of trains or the smooth degree of train speed profile. The model manages to optimize traffic in the transition from a disordered condition (when disruptions appear) to a normal condition (after disruptions) for real-time operations. In conclusion, there are two and three transition phases for the cases without and with entrance delays, respectively, seen by analyzing the deviation between the rescheduled and planned timetables.
As one of the critical state parameters of the battery management system, the state of charge (SOC) of lithium batteries can provide an essential reference for battery safety management, charge/discharge control, and the energy management of electric vehicles (EVs). To analyze the application of deep learning in electric vehicles’ power battery SOC estimation, this study reviewed the technical process, common public datasets, and the neural networks used, as well as the structural characteristics and advantages and disadvantages of lithium battery SOC estimation in deep learning methods. First, the specific technical processes of the deep learning method for SOC estimation were analyzed, including data collection, data preprocessing, feature engineering, model training, and model evaluation. Second, the current commonly and publicly used lithium battery dataset was summarized. Then, the input variables, data sets, errors, and advantages and disadvantages of three types of deep learning methods were obtained using the structure of the neural network used for training as the classification criterion; further, the selection of the deep learning structure for SOC estimation was discussed. Finally, the challenges and future development directions of lithium battery SOC estimation using the deep learning method were explained. Over all, this review provides insights into deep learning for EVs’ Li-ion battery SOC estimation in the future.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.