International Journal of Powertrains (13 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
   
  
• 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
   
  
• Modelling and Analysis of Six-Leg Interleaved Boost Converter for Fuel Cell HEV Applications  
  by Veerendra A.S, Mohd Rusllim Mohamed, Aymen Flah 
  Abstract: This paper proposed a six-leg interleaved boost converter (SLIBC) in an Integrated Multi-Level Converter (IMLC) of switched reluctance motor (SRM) drives for the fuel cell hybrid electric vehicle applications (FCHEV). The fuel cell with the combination of supercapacitors acts as a source in order to observe the speed-torque characteristics of SRM drive. The IMLC is operated with switching positions of the front-end circuit to obtain the different multi-level voltages. The current controlling scheme is employed for speed control of the SRM drive. In the fuel cell driving mode, a supercapacitor is used to provide the phase voltage for quick demagnetisation and excitation. Furthermore, IMLC acts as a 4-level converter for producing multi-level output voltages. The proposed topology has output voltage and current of 875 V and 8.75 A respectively and the speed-torque characteristics are observed as 4200 rad/s and 32 N-m compared to the operation with conventional FLIBC and Modified FLIBC topologies. The effectiveness of the system is validated through MATLAB/Simulink software.
  Keywords: Interleaved Boost Converter; Supercapacitor; Fuel Cell; Hybrid Electric Vehicle; Integrated Multi-Level Converter.
  DOI: 10.1504/IJPT.2026.10071472
   
  
• Formula 1 Race Launch with State-Dependent Phase Optimisation  
  by Marc-Philippe Neumann, Matteo Babin, Giona Fieni, Oliviero Agnelli, Armin Nurkanovic, Alberto Cerofolini, Christopher Onder 
  Abstract: The launch of a Formula 1 race represents a crucial stage that influences the result. It is characterised by multiple consecutive state-dependent phases. At the starting signal, the drivers gradually release the clutch paddle. Then, with a locked clutch, the gear selection determines the subsequent phases. Each phase evolves according to distinct dynamics, while accepting different control inputs. In this work, we propose a framework that jointly optimises those phases. Specifically, we optimise the phase-specific control inputs and the switching times. Results show that decreasing the available battery energy by 0.1 MJ affects the gear shifting strategy and increases the time to cover 140mby 6 ms. This result validates the superiority over sequential phase optimisation, which potentially leads to infeasible results due to its causal nature. For a wet track scenario, we show the duration increase of the phases, while complying with optimal torque deployment for best acceleration.
  Keywords: Nonlinear Optimal Control; Hybrid Dynamical System; Friction Clutch Optimisation; Formula 1; Race Launch Optimisation; State-dependent Phase Optimisation; Hybrid Electric Vehicles.
  DOI: 10.1504/IJPT.2025.10072012
   
  
• Deep Learning-Based Controller Design and Performance Enhancement of BLDC Motor Drive for Electric Vehicle Technology  
  by Anurag Singh, Shekhar Yadav, Sandesh Patel, Nitesh Tiwari 
  Abstract: BLDC motors are mostly used in industrial applications, particularly in electric vehicles (EVs). Nowadays, several studies are being conducted on BLDC motors due to the increasing demand for EVs. The speed performance of the current controller (CC) based BLDC motor on PI, NNF, and CN networks analysis with the help of MATLAB & Simulink software at fixed and variable speeds. The CC CN provides satisfactory speed regulation in compression to conventional PI and CC NNF controllers. The CC CN reduces settling time by approximately 20%, minimises peak overshoot by 15%, and enhances speed response by 25% compared to traditional PI controllers. The present investigation addresses the limitations, including excessive overshoot and sluggish response, of conventional PI controller-based CC solutions in BLDC motors. It suggests utilising NNF and CN networks to combine DL with CC to enhance speed performance, smoothness, and responsiveness.
  Keywords: BLDC motor; PI controller; Deep learning; Current Controller; Neural-net Fitting; Custom Network.
  DOI: 10.1504/IJPT.2025.10072086
   
  
• Energy management techniques for fuel cell hybrid electric vehicles: a critical review  
  by Nitin B. Sawant, A.S. Veerendra, R. Shivarudrawamy, 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 the 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; HEVs; energy management system; EMS; batteries; artificial intelligence.
  DOI: 10.1504/IJPT.2025.10068598
   
  
• Performance evaluation of ant colony optimisation suggested energy management in using HOMER  
  by Pothula Jagadeesh, Asapu Siva, Patil Mounica, Putchakayala Yanna Reddy, Guthikonda Tejaswi, M. Mohamed Thameem Ansari, Md. Azahar Ahmed 
  Abstract: This paper addresses power management in Bhimavaram, India, an educational institution. The institution is known to be commercial load supplied from an 11 KV grid, the load deviations are primarily taken into account during the daytime simply due to the working hours of the institution. This optimisation is accompanied by a bidirectional power transfer from the grid to the institution and the institution to the grid. The optimal energy consumption is suggested for renewable power production for a solar plant with 125 kW generation for increasing the efficient utilisation of renewable energy along with reducing the electricity usage based on fossil fuel. Using an ant colony optimisation (ACO) algorithm, the above-mentioned optimal solution is suggested and that is further validated using HOMER Software. In HOMER Software the validation of the solution given by ant colony optimisation algorithm is completed by simulation.
  Keywords: ant colony optimisation; ACO; solar power plant; HOMER Software; renewable energy; optimal power management.
  DOI: 10.1504/IJPT.2025.10069718
   
  
• Physics-based reverse recovery modelling of ultrafast recovery Si diodes with carrier lifetime control  
  by Imen Abdennabi, Nathalie Batut, Ambroise Schellmanns, Marie-Pierre Chauvat, Fabrice Roqueta, Arnaud Yvon, Sophie Ngo 
  Abstract: Ultrafast recovery diodes are often used in high frequency switching applications because of their ability to switch from the ON-state to OFF-state very quickly. However, ultrafast recovery diode switching performances are very difficult to predict using technology computer-aided design (TCAD) simulation tools, especially when carrier lifetime is adjusted. To model carrier lifetime, the Shockley-read-hall (SRH) recombination theory is used in TCAD tools as a standard simulation model. This model is not sufficient as it considers the presence of only one deep energy level located at the material mid-gap. Used as a carrier lifetime killer, platinum doping introduces several deep energy levels facilitating the minority carrier recombination. Thus, this paper presents a new approach based on trap physical modelling. Trap characteristics are determined using deep level transient spectroscopy (DLTS) measurement technique. This approach can significantly reduce the large mismatch observed between the ultrafast recovery diode turn-off measurements and the standard simulation model results.
  Keywords: ultrafast PIN diode; turn-off transient characteristics; platinum diffusion; minority carrier lifetime control; deep level transient spectroscopy; DLTS.
  DOI: 10.1504/IJPT.2025.10070871
   
  
• Experimental study on cold start strategy of high-power fuel cell system  
  by Juexiao Chen, Yinhao Yang, Chang Du, Ziheng Gu, Tiancai Ma 
  Abstract: With the gradual popularisation of clean energy, proton exchange membrane fuel cells (PEMFCs) have received widespread attention as a good way to apply hydrogen energy. Freezing during start-up of fuel cells in low-temperature environment can cause a series of problems, such as clogging of porous electrodes, obstruction of reactant gas transport, etc. leading to the failure of fuel cell start. Therefore, it is of great research significance to improve the stability and speed of cold start of proton PEMFC without heat source assistance. However, at present, most experimental research on cold start of fuel cells is focused on single cell or low-power stack. In this study, we will conduct start experiments at -5°C~-25°C based on a 130 kW fuel cell power system. The results showed that with the current loading slope 3A/s, the cooling water pump speed 3,000 rpm, the minimum time for automatic purging 50 s, the maximum time for purging 500 s, the impedance real part threshold for purging 750 mΩ, and a manual purge of the air and hydrogen circuits performed before starting, the system used in the study can successfully start at -25°C and then run to a stable operating point.
  Keywords: proton exchange membrane fuel cells; high-power fuel cell system; cold start strategy at -25°.
  DOI: 10.1504/IJPT.2025.10070908
   
  
• Short-term load forecasting technique for power system based on grey correlation analysis and factor analysis  
  by Xiaoguo Zhang 
  Abstract: Aiming at the problem that traditional load forecasting techniques cannot achieve higher forecasting accuracy, a short-term load forecasting technique for power systems based on grey correlation analysis and factor analysis is proposed. Grey correlation analysis is introduced to determine the key load factors, and the forecasting circuit is designed by combining the data from the grid visualisation system. Secondly, the main influencing factors are extracted to form a composite factor by dimensionality reduction through factor analysis. Experiments show that the model performs well in predicting different date types and zone. The difference between the model predicted and true values was less than 0.6%. The results show that the load forecasting technique proposed in the study has high accuracy and stability, which provides a strong support for the stable operation and planning of the power system.
  Keywords: power system; short-term load forecasting; STLF; grey correlation analysis; GCA; factor analysis; grid visualisation management.
  DOI: 10.1504/IJPT.2025.10071545