Forthcoming and Online First Articles

International Journal of Powertrains

International Journal of Powertrains (IJPT)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

Online First articles are published online here, before they appear in a journal issue. Online First articles are fully citeable, complete with a DOI. They can be cited, read, and downloaded. Online First articles are published as Open Access (OA) articles to make the latest research available as early as possible.

Open AccessArticles marked with this Open Access icon are Online First articles. They are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.

Register for our alerting service, which notifies you by email when new issues are published online.

We also offer which provide timely updates of tables of contents, newly published articles and calls for papers.

International Journal of Powertrains (7 papers in press)

Regular Issues

  • Loss Assessment and Experimentation of Gallium Nitride based Integrated Charger for Electric Vehicles   Order a copy of this article
    by Kundan Kumar 
    Abstract: The research on electric vehicles (EVs) and their charging infrastructure are advancing very fast and the adoption of EVs can reduce the burden of fossil fuels, results in a green and healthy environment. The compact sizing and design of an efficient charging system are the major concerns that can be addressed by implementing an onboard integrated charger. The integrated charger works in two modes: the first one is charging mode when the vehicle is in a stationary position while the second one is the propulsion mode in which the vehicle is in motion. In this work, an integrated charger is explored using gallium nitride (GaN)-based semiconductor devices for propulsion as well as charging modes. Further, a scaled-down prototype of 400-watt GaN-based integrated charger is tested for both modes. The various performance results and efficiency of the integrated charger are presented. It is observed that the efficiency of the GaN-based integrated charger is on an average of 45% higher than Si and 23% higher than SiC which shows the dominance of GaN.
    Keywords: efficiency; gallium nitride; integrated charger; MOSFET; power losses; propulsion drive; silicon; silicon carbide.
    DOI: 10.1504/IJPT.2024.10061623
  • Demand Response based Dynamic Economic Load Dispatch in a Microgrid with Modified Red Deer Algorithm   Order a copy of this article
    by Pothula Jagadeesh, Asapu Siva, Srinivas Nakka, Mohamed Thameem Ansari M 
    Abstract: On-site generation microgrids may be a viable option for powering remote areas without grids. These microgrids differ from electricity grid-connected ones. Microgrids without main grid connections employ the same energy management technologies as those connected, including economic dispatch and unit commitment modules. Dynamic economic load dispatch (DELD) in isolated microgrids is the study's goal. These microgrids use load shedding as a last resort to balance supply and demand, allowing responsive loads and renewable electricity to be curtailed. The quantity of electricity made by dispatchable distributed generators (DGs), power cut by renewable DGs, power shed, and demand cut are determined for each time period. The well-known modified red deer optimisation (MRDO) approach is utilised to solve the DELD problem. In a microgrid with two renewable DGs and four dispatchable DGs, MRDO beats PSO, and demand response considerably lowers microgrid maintenance costs.
    Keywords: microgrid; MG; particle swarm optimisation; PSO; demand response; modified red deer optimisation; MRDO; economic load dispatch.
    DOI: 10.1504/IJPT.2024.10063152
  • Linking the performance of a compression ignition engine to physical indications of injector nozzle   Order a copy of this article
    by S.D. Rasika Perera 
    Abstract: In the current study, the effect of aging of the injector nozzle during operation in an automobile engine was investigated by observing the optical imaging of the fuel spray pattern, engine dynamometer testing, and optical imaging of the nozzle sac. In this study, the spray breaks up length and the spray cone angle of four identical sets were compared. The different injector sets were brand new and from an engine of a vehicle that has done 40,000 km, 81,000 km, and 120,000 km. The investigation revealed that as the injector nozzle gets older, the spray cone angle gets reduced and the breakup length increases. Both the reduction of spray cone angle and increased breakup length can be attributed to poor atomisation quality. When the engine is running on old injectors, the fuel economy decreases and harmful emissions increase.
    Keywords: diesel fuel; emissions; soot; fuel injection; spray cone angle; cavitation.
    DOI: 10.1504/IJPT.2024.10060577
  • Evaluation of electrified airpath configurations for an opposed piston two stroke compression ignition architecture   Order a copy of this article
    by Erik Vorwerk, Patrick O'Donnell, Dennis Robertson, Robert Prucka, Benjamin Lawler, Fabien Redon, Ming Huo, Ashwin Salvi 
    Abstract: Opposed piston two-stroke (OP2S) diesel engines have shown promise in reducing emissions and increasing efficiency compared to conventional four-stroke diesel engines. Airpath design on this architecture is critical to realising these benefits, as OP2S scavenging and internal composition are primarily controlled by intake and exhaust pressure differentials, and most pumping work is incurred external to the engine cylinder. Using 1-D simulation and an experimentally validated baseline model, this research evaluates the influence of airpath design on steady-state performance metrics of a two-cylinder OP2S engine. First, conventional, electrified, and novel compression and expansion devices are considered, as well as a range of scavenging control devices, to develop four viable airpath architectures. These architectures are then compared across their operating ranges, and a sensitivity analysis is performed on various airpath component efficiencies. Overall, it is found that the best layout investigated consists of an electrically assisted turbocharger with a variable geometry turbine.
    Keywords: opposed piston two stroke; two-stroke; OP2S; electrified airpath; sensitivity analysis; electrified turbocharger; compression ignition; diesel engine; pumping work; scavenging control.
    DOI: 10.1504/IJPT.2024.10059544
  • Thermal efficiency predictive modelling of dedicated hybrid engines based on an optimal multi-network structure   Order a copy of this article
    by Chengqing Wen, Ji Li, Quan Zhou, Guoxiang Lu, Hongming Xu 
    Abstract: This paper presents an evolutionary data-driven modelling approach for dedicated hybrid engine thermal efficiency (TE) prediction, in which a multi-network structure is developed to further improve the prediction accuracy. This structure enables adaptively segmenting input channels in order to reduce the nonlinearity of data representation in each channel so that the sub-networks can be trained efficiently. In the context, the grey wolf optimisation (GWO) algorithm is applied to find the breakpoints of segmentation for building an optimal multi-network structure. The multilayer perceptron (MLP) is introduced as the basic network due to its simple structure with only two hidden layers. Validated by the experimental data, the accuracy of the multi-network prediction model incorporating GWO improves from 82% to 89%. Also, GWO converges to the optimal solution with 21 iterations compared to 26 for particle swarm optimisation and 31 for the gravitational search algorithm, which demonstrates that GWO has a better performance in this study.
    Keywords: optimal network structure; engine thermal efficiency; grey wolf optimisation algorithm; data-driven modelling.
    DOI: 10.1504/IJPT.2024.10056926
  • Energy consumption optimisation for unmanned aerial vehicle based on reinforcement learning framework   Order a copy of this article
    by Ziyue Wang, Yang Xing 
    Abstract: The average battery life of drones in use today is around 30 minutes, which poses significant limitations for ensuring long-range operation, such as seamless delivery and security monitoring. Meanwhile, the transportation sector is responsible for 93% of all carbon emissions, making it crucial to control energy usage during the operation of UAVs for future net-zero massive-scale air traffic. In this study, a reinforcement learning (RL)-based model was implemented for the energy consumption optimisation of drones. The RL-based energy optimisation framework dynamically tunes vehicle control systems to maximise energy economy while considering mission objectives, ambient circumstances, and system performance. RL was used to create a dynamically optimised vehicle control system that selects the most energy-efficient route. Based on training times, it is reasonable to conclude that a trained UAV saves between 50.1% and 91.6% more energy than an untrained UAV in this study by using the same map.
    Keywords: power consumption; machine learning; reinforcement learning; RL; trajectory optimisation; Q-Learning; energy efficiency; path planning.
    DOI: 10.1504/IJPT.2024.10057473
  • Energy management of hybrid electric vehicle based on linear time-varying model predictive control   Order a copy of this article
    by Daofei Li, Jiajie Zhang, Dongdong Jiang 
    Abstract: Energy management of hybrid electric vehicle (HEV) is crucial for improving fuel economy and reducing emissions. Due to the challenges in both development and implementation, simplified algorithms using rule-based strategies or equivalent consumption minimisation strategy (ECMS), still prevail in real vehicle applications. Taking an HEV with P2 hybrid powertrain for example, a bi-level hybrid model predictive control (bi-HMPC) algorithm is proposed. The upper level calculates the optimal engine/motor torque distribution based on linear time-varying model predictive control (LTV-MPC), while the lower level optimises the gear ratio via hybrid model predictive control (HMPC). The algorithm is preliminarily validated via simulations, which demonstrate that it has better fuel-saving performances than ECMS. Then the LTV-MPC is implemented in real vehicle and validated via dynamometer tests. Results show that it can run real-time and reduce the fuel consumption from 7.05 L/100 km to 6.2 L/100 km, together with noticeable improvements in pollutant emissions.
    Keywords: hybrid electric vehicle; HEV; energy management; linear time varying model predictive control; fuel economy; real vehicle tests.