International Journal of Powertrains (14 papers in press)

Regular Issues

• Implementation of Vehicle to Grid Technology in Microgrid Using MATLAB  
  by Kiran Kumar Kukkamalla, V.N.S.R. Murthy, Mahammad Majahar Hussain, Viswanadhapalli Nagababu, Pothula Jagadeesh, M.D. Azahar Ahmed 
  Abstract: Vehicle to Grid (V-2-G) is a technology which permits electric vehicles (EVs) to draw power through grid for charging and also to reappearance electricity back to the grid when required. V-2-G technology allows electric vehicles to act as mobile energy storage units. When plugged into the grid, an EV can charge its battery, and when necessary, it can discharge electricity back into the grid. This two- way flow of electricity between vehicles and the grid offers several benefits and applications. The growing prevalence of Electric Vehicles (EVs) has prompted an exploration into their viability as energy storage units within micro-grids. This study aims to investigate the utilisation of stationary EVs for effective surplus energy storage, employing bidirectional charging technology. To enhance the dynamic performance of the (V2G-G2V) charging stations, Proportional Integral (PI) control systems are employed. The simulation models are intricately designed to assess both V2G and G2V modes.
  Keywords: Vehicle to grid (V2G); Microgrid ,Bidirectional charging.
  DOI: 10.1504/IJPT.2026.10075754
   
  
• Construction and Application of Optimisation Model for Multi-Object Coordinated Scheduling of Microgrids by Integrating Deep Learning and Game Theory  
  by Yingli Huang, Yinfeng Tang 
  Abstract: In this paper, a multi-agent coordination scheduling optimization model of microgrid based on deep learning and game theory is constructed. The model uses deep learning to accurately predict source load, and combines game theory to deal with the interest coordination problem between multi-agents. The application of the fusion model shows good adaptability and robustness, which can effectively deal with the complexity and variability in the operation of the microgrid. The example provided demonstrates that the model offers notable benefits in boosting microgrid energy efficiency, cutting operational costs, and enhancing system stability. It provides an efficient and intelligent decision support tool for the microgrid scheduling.
  Keywords: Deep learning; Game theory; Microgrid scheduling.
  DOI: 10.1504/IJPT.2027.10078297
   
  
• Range-extended Electric Trucks by Using Low CI Biofuels for Carbon Circulation  
  by Hailong Wang, Marcis Jansons, Xubin Song 
  Abstract: The zero-emission mobility system has a number of bottlenecks with battery electric trucks (BET), though being able to benefit our health, climate and environment. In order to achieve the decarbonised economy more economically, application of low carbon intensity (CI) renewable fuels is a realistic and feasible choice with the select carbon-neutral technologies. In USA, renewable biofuels are one of the affordable motive energy resources to propel vehicles, while carbon-free hydrogen can be another promising fuel in the relatively far future. As such, vehicle electrification with lower CI biofuels such as ethanol can leverage both existing energy resources and fuelling infrastructure nationwide. Based on maturity of engine/powertrain electrification, immediate carbon-reduction contributions could start from a scalable reality today. With the testing data from heavy-duty trucks, the case study will demonstrate technical advantages from those clean alternative biofuels in comparison to carbon-intensive fossil fuels. More specifically, this research focuses on application of ethanol-blended fuels for commercial trucks with a range extender architecture.
  Keywords: truck electrification; BEV; range extender; biofuel; carbon intensity (CI).
  DOI: 10.1504/IJPT.2027.10078687
   
  
• Analysis of powertrain mount system for electric construction equipment vehicle  
  by Ramkumar Kandasamy 
  Abstract: When road-induced vibration frequency matches the powertrain mount’s natural frequency, a poorly designed mounting system can resonate, causing severe vehicle vibrations. Robust mounting design is therefore essential for effective vibration isolation. For electric construction equipment vehicles, superior NVH performance, lower environmental impact, and higher operational efficiency are key targets. The goal of this study is to reduce the transfer of electric powertrain vibration brought on by inertial forces to the supporting structure. A mathematical model representing the drivetrain dynamics is developed to identify optimal mounting configurations. The twelve-degree-of-freedom equations of motion are formulated using Lagrangian methods, yielding rigid-body mode predictions in the range of 2.6-25.5 Hz. Validation against a one-dimensional AMESIM model shows a 58% deviation. Forced-response analysis identifies dominant modes and confirms that higher damping substantially improves isolation. In addition, a GUI tool is implemented to facilitate early-stage design assessment, offering an efficient alternative to computationally intensive multibody simulations.
  Keywords: electromobility; electric powertrain; mount; noise and vibration.
  DOI: 10.1504/IJPT.2027.10079654
   
  
• Off Grid EV Charging using PV System with Diverse MPPT Techniques  
  by Vemulapalli Harika, Gudavalli Madhavi, V.N.S.R. Murthy, Imran Abdul, Veeranna Gangavath, Viswanadhapalli Nagababu, M.D. Azahar Ahmed, Pothula Jagadeesh 
  Abstract: With more people turning to electric vehicles, it really needs dependable charging stations especially in remote places that aren’t connected to the power grid. Solar energy looks like the obvious choice because it’s clean and pretty much everywhere. But there’s an issue that the power obtained from solar panels keeps changing with the weather and sunlight, which can mess with how much energy it can actually harvest. MPPT uses a smart controller, a DC-DC converter paired with an algorithm to fine-tune the system and pull the most power possible from the panels. In this work, off-grid solar EV charging setup is built and tested in MATLAB/Simulink. It has been observed at how it works under typical conditions like standard test conditions and partial shading. The results show that, with a particular MPPT it can reliably charge the EV even in places that are completely off grid.
  Keywords: Off grid EV charging; Maximum power point techniques (MPPT); DC-DC converter; Solar power.
  DOI: 10.1504/IJPT.2027.10079882
   
  
• PMSM Current Predictive Control System Based on Improved Vector Control Algorithm  
  by Manman Li, Lianshuang Yu 
  Abstract: To overcome the dynamic response lag of traditional permanent magnet synchronous motor (PMSM) vector control and the sensitivity of deadbeat predictive control (DPC) to parameter disturbances, this study proposes an improved current prediction method integrating nonlinear disturbance compensation and adaptive weighting. An extended PMSM model considering stator resistance and inductance drift is established, and a nonlinear disturbance observer (NDO) is introduced to construct a compensated prediction model. An adaptive weighted cost function is further embedded to optimize dynamic and steady-state performance. Experimental results under rated and parameter-perturbed conditions show d/q-axis current errors of 0.81.3%, THD of 2.32.5%, low-order harmonic THD of 0.91.1%, current ripple of 0.320.45 A, switching frequency of 10.510.8 kHz, and temperature rise of 4061, achieving high precision, low harmonics, and low losses.
  Keywords: Permanent magnet synchronous motor; Vector control; Deadbeat prediction; Current prediction; Nonlinear disturbance observer.
  DOI: 10.1504/IJPT.2027.10079883
   
  
• Modelling and Component Sizing of a P5 Aftermarket Hybrid Electric Vehicle  
  by Olimjon Tuychiev, Sanjarbek Ruzimov, Akmal Mukhitdinov, Nurmukhammad Abdukarimov 
  Abstract: The present study is devoted to the sizing of electric components such as the electric motor and battery of a P5 hybrid electric vehicle retrofitted with in-wheel motors on the rear axle of conventional vehicles. Utilising a detailed powertrain model in different driving cycles and a simple rule-based control strategy used for practical reasons, the energy consumption of the P5 HEV was evaluated The findings from these simulations suggest that hybridisation leads to substantial energy savings (ranging from 36% to 70%), depending on the driving cycle used. It was found that the benefit increased as the electric motor power increased up to 27.6 kW. Beyond this point, further increases in motor power result in only minor improvements. This motor size therefore represents a trade-off between efficiency gains and system complexity in retrofitted P5 HEVs.
  Keywords: Hybrid Electric Vehicle; P5 configuration; Aftermarket solution; Rule-based control; Component sizing.
  DOI: 10.1504/IJPT.2026.10079928
   
  

Special Issue on: Present and Future Challenges for the Automotive Sector Toward Green Transition

• 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
   
  
• 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
   
  
• Single Cylinder Research Engine Combustion Model with Integrated Laminar Flame Speed Neural Network MetaModel and Knock Induction Time Integral Evaluation  
  by Lorenzo Ferrari, Giuseppe Sammito, Bartosch Jagodzinski, Nicolò Cavina 
  Abstract: This study develops a 0D combustion model for a single-cylinder research engine, integrating a neural network-based laminar flame speed model. Experimental data were obtained with RON95E10 fuel at 23 stoichiometric engine operating points. The neural network, trained on a grid of pressure, temperature, equivalence ratio, and EGR, was coupled with the combustion model, whose turbulent flame speed parameters were calibrated using 18 points and tested on the remaining 5. The model reproduces the centre of combustion and crank angle at maximum pressure with a root mean square error of 1
  Keywords: Combustion modelling Laminar flame speed CFD GT power Knock Induction time integral Neural network SIturb Eddy burn up TPA Gasoline Spark ignition engine.
  DOI: 10.1504/IJPT.2026.10075033
   
  
• A Real-Time Optimal Control Algorithm for Fuel Cell Hybrid Trucks  
  by Max Johansson, Lars Eriksson 
  Abstract: Effective energy management strategies can greatly improve the performance of fuel cell hybrid electric vehicles. In this work, a fast algorithm is proposed to jointly optimize the vehicle velocity and the power split ratio between battery and fuel cell systems. A benchmark problem is solved using traditional dynamic optimization techniques, generating optimal trajectories from which two key characteristics are extracted. The first characteristic suggests the partition of the fuel cell current into a nominal constant level and subsequent deviations as required by transient load conditions. The second characteristic concerns the optimal compressor path considering total system efficiency. The main contribution of this work is a control algorithm developed by exploiting these characteristics. The trajectories generated by it are compared to the benchmark solution, and it is shown that it closely approximates the optimal benchmark at a very low computational cost, suggesting that the algorithm is viable in real-time applications.
  Keywords: real-time; fuel cell; algorithm; optimal control; fuel cell; FCHET; fuel cell hybrid truck; EMS; energy management strategy; heavy-duty; electric vehicles.
  DOI: 10.1504/IJPT.2026.10075298
   
  
• Fuel Cells for On-Road Heavy-Duty Vehicles and the Challenge of Durability and Degradation  
  by Manfredi Villani, Kontorn Thammakul, Giorgio Rizzoni 
  Abstract: On-road heavy-duty vehicles have high power and energy requirements and specific operations needs, which may not be satisfied by current Li-ion battery technology. However, the electrification of this class of vehicles is critical for a green transportation sector. The hydrogen polymer electrolyte membrane (PEM) fuel cell has the potential to facilitate the electrification of heavy-duty vehicles, offering two significant advantages over batteries: short refueling time and longer driving range, avoiding a disruptive impact on fleet operations. In this work, the review of state-of-the-art PEM fuel cells, fuel cell engines, and fuel cell electric vehicles highlights that one of the main challenges hindering their adoption is durability. Therefore, this paper analyzes the degradation in PEM fuel cells through a literature review, with an emphasis on the impact of the operations and duty-cycles of heavy-duty vehicles. Finally, this paper offers a summary of durability tests for PEM fuel cells.
  Keywords: Hydrogen; PEM Fuel Cells; Fuel Cell Electric Vehicles; Heavy-Duty Vehicles; Aging; Degradation.
  DOI: 10.1504/IJPT.2026.10075318
   
  
• A Predictive Algorithm for Enhancing Energy Recovery During Downhills in Hybrid Electric Vehicles  
  by Gianfranco Rizzo, Francesco Antonio Tiano, Nando Caruccio 
  Abstract: Hybrid electric vehicles (HEV) can regenerate electricity during descents. For this energy recovery to be effective, an adequate free capacity in the battery must be available. This can be achieved by adopting control strategies able to ensure future recharging before the descent starts by privileging electric driving and inhibiting engine recharging. A methodology is proposed to determine the amount of energy to be kept free in the battery when the car moves on its usual paths, where most of the energy can be recovered. The proposed strategy, patented, does not use a navigation system, but only low-cost altimetric and GPS sensors combined with a real-time algorithm. The potential of energy recovery is demonstrated through numerous simulations of HEVs running over realistic drive paths, and the results obtained on the road by a prototype built on an Arduino board are presented.
  Keywords: Hybrid Vehicles; Regeneration; Predictive Algorithm; BMS; Downhill; Battery; Electric car; Range anxiety; Energy recovery;.
  DOI: 10.1504/IJPT.2026.10075735
   
  
• A Comparative Study of Ultra-capacitor and Flywheel Systems in electric vehicles under urban and highway drive cycles  
  by Sakher Alaqbawe, Mohammed Ben Tarief, Suleiman Abu-Ein, Wael Adaileh, Nader Aljabarin, Hisham Almujafet 
  Abstract: EV performance is increased and battery life is prolonged by effective energy storage technologies. This study aims to compare two different energy storage systems, ultra-capacitor (UC) and flywheels, regarding their regenerative energy performance in EVs. The objective is to assess the ultra-capacitors and flywheels across driving cycles and states of charge (SOC). The study conducted through MATLAB/Simulink across two driving cycles (FTP-75 and HWFET), at three state of charge (SOC) levels: 25%, 50%, and 75%. Four scenarios were evaluated: the use of an ultra-capacitor alone, the use of a flywheel alone, a hybrid approach utilising both technologies, and a baseline scenario without supplementary storage devices. It was found that ultra-capacitor realised a 4.8% increase in regenerated energy during the FTP-75 cycle, in contrast to the flywheel, which did not provide an enhancement. The results indicate that ultra-capacitors could serve as a more effective option for regenerative braking in urban settings.
  Keywords: Ultra-capacitor; Flywheels; Hybrid energy storage system; Regenerated energy; EV; SOC.
  DOI: 10.1504/IJPT.2026.10079800