Green Energy and Intelligent Transportation最新文献

Lithium-ion battery State of Health (SOH) estimation is an essential issue in battery management systems. In order to better estimate battery SOH, Extreme Learning Machine (ELM) is used to establish a model to estimate lithium-ion battery SOH. The Swarm Optimization algorithm (PSO) is used to automatically adjust and optimize the parameters of ELM to improve estimation accuracy. Firstly, collect cyclic aging data of the battery and extract five characteristic quantities related to battery capacity from the battery charging curve and increment capacity curve. Use Grey Relation Analysis (GRA) method to analyze the correlation between battery capacity and five characteristic quantities. Then, an ELM is used to build the capacity estimation model of the lithium-ion battery based on five characteristics, and a PSO is introduced to optimize the parameters of the capacity estimation model. The proposed method is validated by the degradation experiment of the lithium-ion battery under different conditions. The results show that the battery capacity estimation model based on ELM and PSO has better accuracy and stability in capacity estimation, and the average absolute percentage error is less than 1%.

Railroad condition monitoring is paramount due to frequent passage through densely populated regions. This significance arises from the potential consequences of accidents such as train derailments, hazardous materials leaks, or collisions which may have far-reaching impacts on communities and the surrounding areas. As a solution to this issue, the use of distributed acoustic sensing (DAS)-fiber optic cables along railroads provides a feasible tool for monitoring the health of these extended infrastructures. Nevertheless, analyzing DAS data to assess railroad health or detect potential damage is a challenging task. Due to the large amount of data generated by DAS, as well as the unstructured patterns and substantial noise present, traditional analysis methods are ineffective in interpreting this data. This paper introduces a novel approach that harnesses the power of deep learning through a combination of CNNs and LSTMs, augmented by sliding window techniques (CNN-LSTM-SW), to advance the state-of-the-art in the railroad condition monitoring system. As well as it presents the potential for DAS and fiber optic sensing technologies to revolutionize the proposed CNN-LSTM-SW model to detect conditions along the rail track networks. Extracting insights from the data of High tonnage load (HTL)- a 4.16 ​km fiber optic and DAS setup, we were able to distinguish train position, normal condition, and abnormal conditions along the railroad. Notably, our investigation demonstrated that the proposed approaches could serve as efficient techniques for processing DAS signals and detecting the condition of railroad infrastructures at any remote distance with DAS-Fiber optic cable setup. Moreover, in terms of pinpointing the train's position, the CNN-LSTM architecture showcased an impressive 97% detection rate. Applying a sliding window, the CNN-LSTM labeled data, the remaining 3% of misclassified labels have been improved dramatically by predicting the exact locations of each type of condition. Altogether, these proposed models exhibit promising potential for accurately identifying various railroad conditions, including anomalies and discrepancies that warrant thorough exploration.