International Journal of Powertrains (8 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  
  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  
  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  
  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
   
  
• Performance Improvement of BLDC Motor Driven Electric Vehicle Using Different Metaheuristic Optimisations Based Techniques  
  by Sandesh Patel, Shekhar Yadav, Nitesh Tiwari 
  Abstract: The number of fuel-based vehicles is increasing around the world, which produces greenhouse gases and pollutes air quality. One best solution is using EVs instead of fuel-based vehicles. EVs do not produce any harmful gases that can help keep the air and environment healthy. BLDC motors are gaining more popularity among the automobile sectors such as EVs due to their compact size and fast dynamic response. BLDC motors did not have any brushes but it consisted of mechanical commutators. In this paper, the BLDC motor's speed is controlled by the current controller. A high-performance BLDC motor is designed with the help of meta-heuristic optimisation techniques, namely TLBO, DE, PSO, ABC, and SSO. The role of these optimisation techniques is in determining optimised gain parameters for the PI controller, such as proportional gain (Kp) and integral gain (Ki). The overall system is developed by using MATLAB/Simulink.
  Keywords: electric vehicles; current controller; PI controller; optimisation techniques; driving cycles.
  DOI: 10.1504/IJPT.2024.10065854
   
  
• Efficient Traffic Sign Recognition with YOLOv5  
  by Nacir Omran, Amna Maraoui, Imen Werda, Belgacem Hamdi 
  Abstract: The successful integration of autonomous vehicles into urban environments requires strict adherence to traffic rules and regulations. A critical component for achieving this level of compliance is an efficient and reliable vision system that can accurately recognize and detect traffic signs. In this paper, we propose a comprehensive vision system built upon trained YOLO v5 models for traffic sign classification and detection. Our vision system utilizes the YOLO v5 algorithm, which has been selected based on its exceptional performance in achieving a balance between accuracy and speed which is crucial for real-time applications. To optimize the performance of our detection model, we incorporate transfer learning from the classification model. By leveraging the knowledge gained during the classification task, we enhance the accuracy and reliability of our detection system. This approach allows us to capitalize on the strengths of both models and achieve superior results in traffic sign detection and classification.
  Keywords: Computer Vision; Traffic Sign; Yolo V5.
  DOI: 10.1504/IJPT.2024.10066401
   
  
• State of the Art and some of the Future Challenges for Selectable- and Fixed-Ratio Gear Transmissions  
  by Andas Amrin, Maksat Temirkhan, Hamza Tariq, Amin Amani 
  Abstract: Geared transmissions are as ubiquitous, as they are essential to nearly every mechanical powertrain. There is a large and growing body of research on gear geometry, with a focus on manufacturing, errors and accuracy, on gear strength, lubrication, efficiency, noise and vibration, for practically all known embodiments of parallel axes-, intersecting axes- and non-intersecting axes- spatial configurations. Configurations of planetary transmissions are also studied extensively -not least in terms of topology due to their importance to automatic transmissions-, and to a lesser but growing extent also cycloidal transmissions, strain-wave transmissions and other exotic but singular architectural configurations are subject to continued research and development. This research and development landscape is far from exhaustive, however; we argue that an extensive part of the -at this time conceivable- design space is largely unexplored: not only in terms of gear contact geometry and associated kinematic and dynamic response and function, but mostly in terms of topology and architecture, either alone or in combination with geometry. We reason, map and anticipate significant innovations and lay a roadmap for future research and technology development. We also make the essential links to use cases from the fields of aerospace engineering, automotive engineering, robotics, and energy.
  Keywords: Power transmissions; motion transmission; gear; geometry; topology.
  DOI: 10.1504/IJPT.2024.10066563
   
  
• Establishment and Calibration of Performance Simulation Model for Hydrogen Injector of Fuel Cell  
  by Runing Li, Xing Feng, Zuyong Yang, Jian Zhang 
  Abstract: In order to meet the demand of the PEMFC digital prototype for high-precision simulation models of its key components, based on the analysis of the structure and working principle of the hydrogen injector, the mathematical model of the hydrogen injector was established according to the continuity equation and energy equation of gas flow. On this basis, the one-dimensional performance simulation model of the hydrogen injector was developed using Python computer language; a test bench for nozzle injection characteristics was setup. Compressed air was used to replace hydrogen. The nozzle injection characteristics were tested under different inlet and outlet pressures, and the test data of nozzle gas mass flow were obtained; through the test data and nozzle gas flow model, the orifice flow coefficient was determined to be 0.89, and the one-dimensional performance simulation model of hydrogen injector was calibrated; the accuracy of the one-dimensional performance simulation model of hydrogen injector is verified through various working conditions, and the accuracy of the simulation data can reach 97%, which lays a foundation for the development of the digital prototype of PEMFC.
  Keywords: proton exchange membrane fuel cell; hydrogen injector; nozzle; digital prototype; model calibration.
  DOI: 10.1504/IJPT.2024.10066843
   
  
• Hydrogen fuelled Stepped Piston Engine for Ultra-Low Emissions Hybrid and Range Extender Electric Vehicles  
  by Peter Hooper 
  Abstract: Compact two-stroke cycle engines are often perceived to suffer from poor durability, however that is not the case for segregated scavenge two-stroke engines. Stepped piston engines successfully separate lubrication and air delivery systems and allow closer correlation with the high durability aspects of four-stroke engines than the challenges associated with conventional two-stroke engines. This study builds upon previous experimental testing and 1-d CFD modelling research performed on a stepped piston segregated scavenge engine. Improved performance compared with the previous research using gasoline/indolene fuels, together with an initial exploratory investigation into the benefits of hydrogen fuel operation is presented. The drive of the study is to present an ultra-low emission solution for Hybrid Electric and Range Extender Electric Vehicles, whilst simultaneously countering the cost issues of such vehicles. The potential to overcome the continuing concerns of consumers in terms of the serious range anxiety challenges is discussed.
  Keywords: Hydrogen; Hybrid Electric Vehicle; Range Extender Electric Vehicle; stepped piston engine; two-stroke cycle engine; four-stroke cycle engine; engine modelling/simulation.
  DOI: 10.1504/IJPT.2024.10066892