Forthcoming and Online First Articles

International Journal of Powertrains

International Journal of Powertrains (IJPT)

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International Journal of Powertrains (3 papers in press)

Regular Issues

  • Comparative analysis of the ISO Tolerance class of 3D-printed spur cylindrical gears produced with Material Extrusion (MEX) and Powder Bed fusion (PBF) techniques   Order a copy of this article
    by Christos Vakouftsis, Georgios Vasileiou, Georgios Kaisarlis, Christos Kalligeros, Christos Papalexis, Pavlos Zalimidis, Christopher Provatidis, Vasileios Spitas 
    Abstract: The present work correlates the printing accuracy of two major 3D-printing techniques applied for the production of spur gears by calculating the ISO tolerance class that defines the accuracy level of gears in all industrial applications. Specimens were produced using material extrusion MEX-TRB/P/ABS and powder bed fusion PBF-LB/P/PA22 techniques, evaluated by using a touch-probe coordinate measuring machine (CMM) with appropriate software and finally compared in terms of standards gear geometry errors and deviations. MEX-TRB produced specimens exhibited higher levels of accuracy and ISO quality class correspondingly. Both sets of specimens were found to comply with ISO Q12 or higher that greatly varies from the quality class of most metallic gears used in industrial applications. The printing parameters are detailed and discussed extensively in an attempt to provide insight on further research and process optimisation for the production of AM gears.
    Keywords: gears; 3D printing; material extrusion; MEX; powder bed fusion; PBF; ISO tolerance class; CMM; gear quality.
    DOI: 10.1504/IJPT.2024.10064740
     
  • Distributed Multiobjective Predictive Control for Connected Electric Vehicles   Order a copy of this article
    by Yanhong Wu, Zhaofeng Gao, Xiulan Song, Ji Li, Quan Zhou, Hongming Xu 
    Abstract: This paper proposes a distributed multi-objective predictive control (DMPC) strategy to balance the conflicts among driving safety, comfort and economy of vehicular platoon. A vehicular platoon model is established to describe the characteristics of connected electric vehicles. Then, a distributed model predictive controller is developed for vehicular platoon. To reduce the multi-objective conflicts, a weighted-sum-based optimisation method is designed and inserted into the controller. Furthermore, the stability and the iterative feasibility of the proposed strategy is proven. Finally, several experiments are carried out on a self-developed co-Simulink platform with the IPG-Carmaker. Compared with the centralised multi-objective predictive control method, the proposed DMPC strategy distributed multi-objective predictive control method exhibits a 7.69% enhancement in driving safety, a 4.64% improvement in driving comfort and 3.70% advancement in driving economy for platoon.
    Keywords: vehicular platoon; multi-objective conflicts; predictive control; IPG-Carmaker.
    DOI: 10.1504/IJPT.2024.10064787
     
  • Performance Analysis of Driving Style Identification by using Wolf-Inspired Evolutionary Clustering   Order a copy of this article
    by Chengqing Wen, Cameron Gorle, Ji Li, Quan Zhou, Hongming Xu 
    Abstract: Driving style identification is an essential task in vehicle technology to improve security, driving experience, and customise targeted energy management strategies. Commonly used clustering algorithms, like K-means plus, occasionally suffer from convergence to local optimum and unreasonable setting of initial centroid placement. This paper proposed a neural wolf-inspired evolutionary clustering method for driving style identification. Driving styles show distinctive features in a time series, thus the fast Fourier transform (FFT) is used to convert time series data into energy data corresponding to the main frequency in the frequency domain. Then grey wolf optimisation (GWO) algorithm as a bio-inspired optimisation algorithm is formulated to search the global optimal clustering centroids. The proposed algorithm is compared with K-means plus, Gaussian mixture model (GMM), and PSO-inspired clustering method to identify three distinct driving styles. The data investigated were collected on a driver-in-the-loop intelligent simulation platform. Silhouette coefficient was selected to evaluate the clustering effect of the test dataset in the clustering model implemented by the proposed algorithm, its five times average reached 0.9531, which is 0.0280 higher than K-means plus, 0.0124 higher than GMM, and 0.0106 higher than PSO-inspired clustering method.
    Keywords: driving style identification; grey wolf optimisation; GWO algorithm; clustering algorithm; fast Fourier transform; FFT; Gaussian mixture model; GMM.
    DOI: 10.1504/IJPT.2024.10065486
     

 

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