Abstract:With the development of electrified transportation, electric vehicle positioning technology plays an important role in improving comprehensive urban management ability. However, the traditional positioning methods based on the global positioning system (GPS) or roadside single sensors make it hard to meet requirements of high-precision positioning. Considering the advantages of various sensors in the cooperative vehicle-infrastructure system (CVIS), this paper proposes a compound positioning method for connect… Show more
“…In [45] the authors present the benefits of the cooperation of different sensors in the so-called Cooperative Vehicle-Infrastructure System (CVIS) in the context of Connected Electric Vehicles (CEVs). The approach is based on the merging of different data coming from different devices in supporting the cooperative system.…”
In this paper, a Cooperative Multi-technology Simultaneous Localization and Signal Mapping (CM-SLASM) technique is proposed to improve the signal map accuracy and to build on-thefly a signal map to be used also in indoor environments where conditions can change over time. Moreover, the CM-SLASM combines WiFi, Ultra WideBand (UWB) and LIght Detection And Ranging (LIDAR) signals to improve positioning estimation by sharing information and cooperation among vehicles through Vehicle-To-Vehicle (V2V) communication links. In particular, LIDAR-based distance between vehicles is shared among neighbor vehicles to improve the vehicle positioning estimated by an Extended Kalman Filter (EKF) where WiFi fingerprinting is combined with UWB multilateration. The overall solution where EKF estimation allows to build more precise signal MAP is validated by simulation in a defined indoor scenario where vehicles equipped with different percentages of LIDAR, and different quantities of UWB and WiFi emitters have been considered. The proposed strategy has been validated through an extensive simulation campaign in various scenarios of interest and through a real-world experiment conducted in a laboratory test environment.
“…In [45] the authors present the benefits of the cooperation of different sensors in the so-called Cooperative Vehicle-Infrastructure System (CVIS) in the context of Connected Electric Vehicles (CEVs). The approach is based on the merging of different data coming from different devices in supporting the cooperative system.…”
In this paper, a Cooperative Multi-technology Simultaneous Localization and Signal Mapping (CM-SLASM) technique is proposed to improve the signal map accuracy and to build on-thefly a signal map to be used also in indoor environments where conditions can change over time. Moreover, the CM-SLASM combines WiFi, Ultra WideBand (UWB) and LIght Detection And Ranging (LIDAR) signals to improve positioning estimation by sharing information and cooperation among vehicles through Vehicle-To-Vehicle (V2V) communication links. In particular, LIDAR-based distance between vehicles is shared among neighbor vehicles to improve the vehicle positioning estimated by an Extended Kalman Filter (EKF) where WiFi fingerprinting is combined with UWB multilateration. The overall solution where EKF estimation allows to build more precise signal MAP is validated by simulation in a defined indoor scenario where vehicles equipped with different percentages of LIDAR, and different quantities of UWB and WiFi emitters have been considered. The proposed strategy has been validated through an extensive simulation campaign in various scenarios of interest and through a real-world experiment conducted in a laboratory test environment.
“…Data augmentation is a helpful approach for designing an ML model since it allows researchers to increase the size of the learning data without having to acquire new data [125]. Fusion of multi-source dataset, including historical velocity data, video, or image information, also can significantly enhance the training and performance of ML models in optimizing energy consumption [179]. By combining different data sources, the model can capture a more comprehensive understanding of the underlying patterns and relationships within the energy system.…”
Electric vehicles are growing in popularity as a form of transportation, but are still underused for several reasons, such as their relatively low range and the high costs associated with manufacturing and maintaining batteries. Many studies using several approaches have been conducted on electric vehicles. Among all studied subjects, here we are interested in the use of machine learning to efficiently manage the energy consumption of electric vehicles, in order to develop intelligent electric vehicles that make quick unprogrammed decisions based on observed data allowing minimal electricity consumption. Our interest is motivated by the adequate results obtained using machine learning in many fields and the increasing but still insufficient use of machine learning to efficiently manage the energy consumption of electric vehicles. From this standpoint, we have built this comprehensive survey covering a broad variety of scientific papers in the field published over the last few years. According to the findings, we identified the current trend and revealed future perspectives.
“…Multi-source sensor information fusion for mechanical fault diagnosis is still in its infancy due to its complexity and feature extraction and fusion issues [24,25]. Due to the rapid development of deep learning-related research results, designing a complete fault diagnosis system based on multi-source information fusion using deep learning to realize the algorithm structure, including data preprocessing, classifiers, and evidence fusion, has become important [26,27]. Ghosh et al [28] discovered an evidence theory and multiinformation fusion to effectively-identified composite faults and provided maintenance instructions for composite fault diagnosis by fussing several maximal statistical distances and identifying fault classes technique.…”
In machine intelligence fault diagnostic and health status decision-making systems, rich, complex, and fuzzy feature information cannot facilitate fault decision-making merely on a single data source. This requires utilizing the heterogeneity of information gathered from multiple sources to diminish the system's uncertainty and improve the accuracy of decision-making. In this work, a novel neural network-based multi-source fusion classification model is proposed to diagnose the pump mechanical faults. The Multi-head Attention D-S evidence fusion (MADS) system extends the model's ability to focus on rich features. Furthermore, the Uncertain Values Throwing Mechanism (UVTM) can effectively eliminate samples from uncertain categories and increase the model's ability to distinguish diagnostic results with low confidence. Compared with a single sensor, our multi-sensor joint decision based on 7 sensors considerably improved the fault diagnostic accuracy of MADS system, which has increased by at least 12.34%. Experimental validation demonstrates that utilizing the improved combination rules provided for multi-source evidence fusion fault diagnosis can significantly improve the efficacy of conventional D-S fusion and reduce the probability of misjudgment; combining the multi-head attention mechanism can dramatically increase the precision of model fault diagnosis. The proposed method has the potential to substantially accelerate research in the field of multi-source sensor joint fault diagnosis. Keywords: Mechanical Fault Diagnosis, Pump fault diagnosis, Evidence fusion, Multi-head Attention
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