International Journal of Powertrains (15 papers in press)

Regular Issues

• 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
   
  
• Innovative Energy Control Strategy for Standalone SPV-DG-Battery Power Systems Employing Fractional Order PID Controllers  
  by M.D. Azahar Ahmed, Gudavalli Madhavi, Vemulapalli Harika, Srinivasa Nakka, Mahammad Majahar Hussain, Pothula Jagadeesh 
  Abstract: A standalone Solar Photovoltaic (SPV), Diesel Generator (DG), and Battery Pack Unit (BPU) system is controlled using a new method. The setup uses a bidirectional inverter to connect the PCC to the DC bus and a DC-DC converter to connect the DC bus to the BPU. This study focuses on weather and partial shading-induced SPV output power oscillations. A control strategy using Fractional Order Proportional Integral Derivative (FOPID) controllers for the inverter and DC-DC converter is developed to improve during disturbances. Management of the DC-DC converter ensures SPV,DG and BPU active power sharing. Even with imbalanced load situations, the inverter control maintains balanced PCC voltages and DG currents, reducing mechanical stress on the DG shaft and diesel consumption and the inverter sends reactive power to the load, reducing the DG's reactive power burden and fuel consumption. Comprehensive Matlab and RTDS simulation support the proposed approach.
  Keywords: Solar PV,Battery Pack Unit,Diesel Generator,Fractional Order PID Controller,Real Time Digital Simulator.
  DOI: 10.1504/IJPT.2026.10075123
   
  
• Deep Reinforcement Learning-Based Multi-Objective Energy Management for Fuel Cell Heavy-Duty Trucks  
  by Zhongwen Zhu, Dongying Liu, Weihai Jiang, Zirui Zhang, Cheng Li, Chuanlong Ji 
  Abstract: Fuel cell hybrid electric vehicles (FCHEVs) have emerged as a key research focus due to low carbon emissions, high energy efficiency, and low noise, yet optimising energy management strategies (EMS) remains challenging. This study targets fuel cell health and hydrogen-electric efficiency, leveraging neural networks and deep reinforcement learning (DRL) to improve EMS. A full-vehicle Simulink model for a fuel cell heavy-duty truck (FCHT) was established based on experimental data, coupled with a fuel cell lifetime degradation model. A multi-objective optimisation strategy was designed via the deep deterministic policy gradient (DDPG) algorithm. Offline comparisons and hardware-in-the-loop (HIL) experiments verified its superiority: 0.57% total operating cost error and 0.7% state of charge (SOC) deviation, significantly enhancing fuel cell lifespan and economy-health synergy. This work provides a novel approach for FCHT EMS optimisation, facilitating FCHEV commercialisation and the transportation sectors low-carbon transition.
  Keywords: Fuel cell heavy-duty truck; Energy management strategy; Fuel cell lifetime degradation model; Deep reinforcement learning; Hardware-in-the-loop.
  DOI: 10.1504/IJPT.2026.10075124
   
  
• Optimisation of MPPT Techniques: Conventional, AI-Based, and Hybrid Approaches in Photovoltaic Systems  
  by Abdelkarim Ballouti, Hatim Ameziane, Mohamed Chouiekh, Youness El Mourabit, Alia Zakriti 
  Abstract: Maximum power point tracking (MPPT) techniques are crucial for PV systems, which are sensitive to irradiation and temperature variations. This research introduces a high-performance hybrid approach that boosts efficiency by combining the perturb and observe (P&O) technique with artificial neural networks (ANN) to enhance PV power extraction. The hybrid ANN-P&O algorithm dynamically adjusts the step size based on real-time solar irradiance conditions, making the tracking process more adaptive and efficient. To evaluate this approach, a PV system is simulated under varying weather conditions. The hybrid methods performance is compared to standard P&O and ANN techniques using MATLAB/Simulink simulations. Results highlight the enhanced response time of the hybrid ANN-P&O method (0.16 s), demonstrating a faster and more stable tracking process compared to ANN (0.18 s) and P&O (0.24 s). Additionally, the hybrid ANN-P&O achieves 98% efficiency, outperforming ANN (96%) and P&O (95%). These findings confirm the superiority of hybrid strategies in optimising energy output, ensuring higher power conversion and greater system stability for photovoltaic applications.
  Keywords: Photovoltaic (PV); Maximum power point tracking (MPPT); Perturb and Observe (P&O); Artificial Neural Network (ANN); DC-DC converter.
  DOI: 10.1504/IJPT.2026.10075160
   
  
• 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
   
  
• Development of K-Nearest Neighbours Model for Diagnosing Vehicle Automatic Transmission Failures  
  by Anh Tuan Pham, Van Tra Nguyen, Ngoc Tuan Vu, Van Tu Nguyen 
  Abstract: This study investigates the operational conditions of automobiles in Vietnam and the standard automatic transmission (AT) failures that occur during their operation. Various types of vehicle automatic transmission failures, alongside normal operating conditions, are simulated in Simulation-X. The research involves data pre-processing and exploratory data analysis to identify appropriate models for classification. A comprehensive review of machine learning classification algorithms and hyperparameter tuning uses simulation datasets. The KNN model was trained and evaluated, achieving 92.2% accuracy on the test dataset. Permutation importance was evaluated using the open-source library scikit-learn. Potential improvements of the model classifiers are discussed, and recommendations are provided based on the findings. The results demonstrate that the proposed approach can effectively classify AT failures, supporting the development of software modules for real-time technical state supervision and the design of a test bench for assessing AT reliability.
  Keywords: diagnose automatic transmission; failures; acceleration time; vehicle speed; vehicle power; Simulation-X; machine learning; classification; k-nearest neighbours; hyperparameter tuning.
  DOI: 10.1504/IJPT.2026.10075333
   
  
• Renewable Energy Management in Microgrid with Demand Response using Adaptive Particle Swarm Optimisation  
  by Pothula Jagadeesh, Asapu Siva, Patil Mounica, Tejaswi G, Mohamed Thameem Ansari M, M.D. Azahar Ahmed 
  Abstract: Effective energy management of renewable energy sources (RES) on micro grids (MGs) presents opportunities and problems. In this research, we offer an Adaptive Particle Swarm Optimization (APSO)-based scheduling model for dynamic and stochastic renewable generation, load demands, and market energy prices. This suggested APSO model optimizes battery storage, controlled load schedule, and energy balance at least operational cost. Compared to comprehensive evaluations, APSO excels at handling uncertainties, achieving rapid convergence, and remaining robust and reliable in various issue contexts. Comparing APSO to various metaheuristic optimisation methods shows its improved adaptability, efficiency, and efficacy. It tackles inadequacies in current micro grid operations practice by combining probabilistic modelling, demand response integration, and multi carrier energy systems to create a comprehensive framework for sustainable and economically optimal micro grid operation.
  Keywords: Micro grid; Renewable Energy Systems; Adaptive Particle Swarm Optimization (APSO); Demand Response; Energy Management; Cost Optimisation; Probabilistic Modelling.
  DOI: 10.1504/IJPT.2026.10075587
   
  
• Implementation and Validation of Fuzzy Logic based Maximum Power Point Tracking with Cuk Converter for PV Applications  
  by Veeranna Gangavath, Vemulapalli Harika, Gudavalli Madhavi, Kambampati Lakshmi, Viswanadhapalli Nagababu, M.D. Azahar Ahmed 
  Abstract: This paper develops Maximum Power Point Tracking (MPPT) using fuzzy logic control (FLC) and a Cuk converter to improve photovoltaic (PV) system efficiency. The suggested system dynamically adjusts the converter's duty cycle to maximize PV panel power extraction in variable irradiance and temperature. Comparing FLC-based MPPT to classic Perturb and Observe (P&O) shows its superiority in tracking accuracy, response speed, and durability, especially in quickly changing situations. FLC computes duty cycle modifications using error and change-in-error inputs to minimize oscillation near the maximum power point. MATLAB/Simulink simulations and PIC microcontroller hardware implementation verify real-time adaptability and operational efficacy. The experimental results show that FLC reduces power loss compared to standard methods, boosting renewable energy system outputs.
  Keywords: PV Systems; Fuzzy Logic Control; Maximum Power Point Tracking; Cuk Converter; hardware Implementation.
  DOI: 10.1504/IJPT.2026.10075646
   
  
• A Multi-objective Optimisation Scheduling Method for Microgrids Based on Improved Sparrow Algorithm  
  by Han Zhou 
  Abstract: This study investigates multi-objective optimization scheduling for microgrid clusters to enhance power operation, energy management, and reduce costs. A multi-objective optimization model is developed and solved using an improved sparrow search algorithm incorporating Bernoulli chaotic mapping, Cauchy mutation, dynamic adaptive weights, and reverse learning. Simulation results show that the algorithm converges after about 88 iterations with a search accuracy above 92.05% and a maximum deviation within 2%. In a 24-hour grid-connected scheduling scenario, the total operating cost of the microgrid cluster decreased from 31.82
  Keywords: Sparrow algorithm; Microgrid; Energy management; Operating costs; Power dispatch.
  DOI: 10.1504/IJPT.2026.10075650
   
  
• SOCE Estimation of Lithium-Ion Batteries for New Energy Vehicles based on Improved AEKF  
  by Pinjia Shi, Qiongqiong Liu, Jie Wen 
  Abstract: Lithium-ion batteries are widely used in new energy vehicles due to their high energy density and long cycle life. However, their performance and safety can degrade under various conditions. To address this, a novel lithium-ion battery state estimation method based on an improved adaptive extended Kalman filter (AEKF) algorithm is proposed. The method enhances estimation accuracy by introducing multiple optimization mechanisms to the traditional AEKF. Experimental results show that the proposed method reduces noise bias by 3.3%, 1.9%, and 5.9%, and lowers deviation in cycling tests by 0.7%, 3.7%, and 5.2%, respectively, compared to traditional approaches. Optimal performance is achieved at a time scale of 10 and temperature of 25??. The method demonstrates superior accuracy and robustness in estimating battery states, making it valuable for improving the reliability and efficiency of lithium-ion batteries in new energy vehicles.
  Keywords: AEKF; New energy vehicles; Lithium-ion battery; SOC estimation; Parameter identification.
  DOI: 10.1504/IJPT.2026.10075721
   
  
• 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
   
  
• 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