International Journal of Powertrains (7 papers in press)

Regular Issues

• 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
   
  
• 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
   
  
• Data-Driven Modelling of Battery State-of-Health using Multi-Criteria-Based Feature Reduction  
  by Abdul Azis Abdillah, Cetengfei Zhang, Zhong Ren, Ji Li, Hongming Xu, Quan Zhou 
  Abstract: Previous studies have explored many features that can be used to estimate the health of lithium batteries. However, there are still gaps from previous research, namely, which features should be used and which can be ignored to make the best SOH estimation using machine learning models. This paper proposes a multi-criteria-based feature reduction method to find and combine the best features with machine learning models for estimating the lithium ion battery SOH. This research consists of three main stages: first, determining the features that will be used for building the model, these features include voltage, current, temperature, and time; secondly, carrying out multi-criteria-based feature reduction, existing features are selected based on a combination of four methods such as Pearson correlation rank, Lasso regression, sequential feature selection (SFS), and PCA; third, using the selected features to test the battery health estimation performance using multi-layer perceptrons. The results show that the proposed multi-criteria-based feature reduction method can determine useful features, thereby increasing the generalisation ability and accurate prediction results for lithium-ion battery health degradation under actual EV usage conditions. Besides, the proposed method combined with MLP can outperform other models to 40% of R2.
  Keywords: lithium-ion battery; SOH; Multi-Criteria-Based Feature Reduction; Machine Learning.
  DOI: 10.1504/IJPT.2024.10067235
   
  
• Energy Management Techniques for Fuel Cell Hybrid Electric Vehicles: a Critical Review  
  by Nitin B. Sawant, Veerendra A.S, Shivarudrawamy R, Yogesh V. Mahadik, Aymen Flah 
  Abstract: This article presents a hybrid energy system using fuel cells (FCs). The shortcomings of the pure fuel cell vehicle able to be made up for by the fuel cell hybrid electric vehicle (FCHEV), which combines different energy sources. However, the operation mode of the power system becomes more intricate due to the presence of different sources of energy. Consequently, one of the key tools for the FCHEV is developing methods to run various energy sources efficiently and consistently. This work introduces a methodical arrangement of FCHEV's topologies and discusses that characteristics of different structures. Next, an examination of the FCHEV's energy management strategies (EMSs) in comparison is given, encompassing rule-based, optimisation-based, and advanced learning-based EMSs. Lastly, studies on fuel cell energy management conducted over the past ten years is summed up based on various topologies and EMSs, and the statistical analysis approach is used to further assess the development trend of EMSs. For researchers working on EMSs, this serves as a useful framework for summarising the present issues and advancement patterns of EMSs.
  Keywords: Fuel Cells; Hybrid Electric Vehicles; Energy Management system; Batteries; Artificial Intelligence.
  DOI: 10.1504/IJPT.2025.10068598
   
  
• Reality Survey on Electrifying Class 8 Trucks with Scaleup Goals in USA  
  by Hailong Wang, Youming Tang, Mao Pang, Xubin Song 
  Abstract: Vehicle electrification transition is gaining the momentum across diverse markets worldwide, though there are shared major concerns with using or owning an electric vehicle (EV) regarding price, battery range, battery lifetime, and charging infrastructure. This report provides a distinctive perspective of electrifying heavy-duty trucks (HDT) in USA, while there is a global consensus that the trucking industry is a hard-to-abate sector for emission reduction because of the salient reliance on diesel engines. Specifically, a succinct overview of the charging infrastructure in USA is sorted out per the publication information. Then the charging systems from the grid to an EV are briefed to underscore the enormous challenge of upgrading the grid capacity to meet much higher charging power requirements for HDTs' daily operation than light-duty family cars at home. In this paper, exemplary details about the difficult task of HDT electrification are further examined through the industry-leading electric tractors (Class 8) from Tesla, which are now commercialization-ready for scale application in the North American market. Finally, reality investigation will be further elaborated according to the analytic cases studied in this paper.
  Keywords: heavy-duty truck (HDT); Tesla Semi; zero emission vehicle (ZEV); Megacharger; battery.
  DOI: 10.1504/IJPT.2025.10069007
   
  
• Performance Evaluation of Ant Colony Optimisation Suggested Energy Management in using HOMER  
  by Pothula Jagadeesh, Asapu Siva, Patil Mounica, Putchakayala Yanna Reddy, Tejaswi G, Mohamed Thameem Ansari M, M.D. Azahar Ahmed 
  Abstract: This paper addresses the management of power in Bhimavaram, India, an educational institution. The institution is known to be commercial load supplied from an 11KV grid, the load deviations are primarily taken into account during the daytime simply due to the working hour of the institution. This optimisation is accompanied by a bidirectional power transfer from grid to institution and institution to grid. The optimal energy consumption is suggested for renewable power production for a solar plant with 125kW generation for increasing the efficient utilisation of renewable energy along with reduce the electricity usage based on fossil fuel. Using Ant Colony optimization algorithm, the above mentioned optimal solutioned is suggested and that is further validates using Homer Software. In Homer software the validation of the solution given by Ant Colony Optimization algorithm is completed by simulation.
  Keywords: Ant Colony Optimization; Solar power plant; HOMER software; Renewable Energy; Optimal Power Management.
  DOI: 10.1504/IJPT.2025.10069718