International Journal of Powertrains (5 papers in press)

Regular Issues

• 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
   
  
• Model-Based Co-Design of a Generic Fuel Cell Hybrid Vehicle Via Heuristic Optimisation Algorithms  
  by Paolo Aliberti, Camilo Andrès Manrique Escobar, Marco Sorrentino, Cesare Pianese 
  Abstract: Fuel cell hybrid electric vehicles offer a compelling alternative to traditional thermal engines and fully electric propulsion systems due to their zero emissions and extended range. Enhancing these benefits involves the co-design of the powertrain and control strategies. For a light-duty fuel cell vehicle, co-design is performed here via heuristic algorithms, maximising fuel economy. Moreover, initial conditions, which often limit convergence performance, are carefully selected. A flexible control strategy is embedded in the procedure, enabling simultaneous adaptation to the currently investigated powertrain configuration. Considering five consecutive WLTP cycles, two scenarios are investigated, differing by the admitted post-driving recharge time. 115.12 km/kg fuel economy is achieved, with a 2% improvement in the unconstrained case, which also enables a 47% downsizing of the fuel cell system. The final outcome, proved via comparison with dynamic programming, is that higher degree of hybridisation shall be preferred, especially if post-driving battery recharging is assumed.
  Keywords: Proton exchange membrane fuel cell; hybrid vehicle; model-based co-design; finite state-machine control.
  DOI: 10.1504/IJPT.2025.10073239
   
  
• Intelligent Motor Drive Systems for Electric Vehicles: Energy-Efficient Control of Induction Machines Using Field-Oriented Strategies  
  by Tanushree Mistry, Sudhansu Kumar Samal, Smitanjali Rout, Bijaya Paikray 
  Abstract: Electric Vehicles (EVs) are revolutionising the transportation landscape by offering a cleaner alternative that significantly reduces environmental impact. To boost their overall efficiency, ongoing research focuses on selecting the most appropriate motor drive systems and designing effective control strategies. The motor and its associated drive are central to EV functionality, acting as the core components of propulsion. This review evaluates several electric machine types to determine the most efficient option for EVs. Induction motors (IMs) have proven to be a practical and robust choice, representing roughly 3035% of EVs following permanent magnet synchronous motors (PMSMs) and brushless DC motors (BLDCs). Advances in control methodologies, particularly the adoption of field-oriented control (FOC), have significantly improved the dynamic response, torque precision, and energy efficiency of IMs. Additionally, the integration of artificial intelligence (AI) into motor control has introduced intelligent, adaptive systems capable of responding effectively to real-time driving conditions.
  Keywords: Electric Vehicles (EVs); Induction Motors (IMs); Motor Drive Systems; Field-Oriented Control (FOC); Electric Machine Selection; Energy Efficiency; Propulsion Systems; Adaptive Control.
  DOI: 10.1504/IJPT.2025.10073467