International Journal of Mathematical Modelling and Numerical Optimisation (30 papers in press)

Regular Issues

• Multi-modal emotion recognition from speech, facial expression, video and text modalities: a review  
  by Ruchi Chauhan, Nirmala Sharma, Ajay Sharma 
  Abstract: Multi-modal emotion recognition, which leverages the combined strengths of speech, facial expressions, video, and textual information, has become a cornerstone in the development of emotionally intelligent technologies. By capturing the complexity of human emotions more accurately than single-modality systems, multi-modal approaches offer transformative benefits across various sectors of society. In mental health, they enable early detection of emotional distress and support remote psychological monitoring. In education, emotion-aware systems foster adaptive learning environments tailored to students' emotional states. Public safety applications benefit from improved behavioral analysis, while customer service is enhanced through more empathetic and personalized interactions. This review presents a comprehensive overview of recent progress in emotion recognition from each modality over the past decade, focusing on innovative methods for extracting, combining, and interpreting emotional signals.
  Keywords: multi-modal emotion recognition; MER; speech emotion recognition; facial emotion recognition; video emotion recognition; text emotion recognition; machine-human interaction.
  DOI: 10.1504/IJMMNO.2026.10073643
   
  
• Modelling, optimisation, and experimental evaluation of predictive maintenance strategies for LiFePO₄ battery health in e-bikes  
  by Appalabathula Venkatesh, Simhadri Phani Kumar, Tousif Khan Nizami 
  Abstract: The rapid expansion of the electric vehicle (EV) sector has heightened the need for advanced battery management systems (BMS) to ensure lithium-based batteries safety, reliability, and efficiency. This study presents the design and real-time implementation of a 72 V, 36A h LiFePO₄ e-bike battery pack integrated with an active balancing BMS. This research study uniquely combines the real-world GPS-tracked e-bike testbed, evaluated for range testing, and data were collected across varying terrains, speeds, and riding conditions. BMS data voltage, current, temperature, and health indicators are analysed to identify operational trends, deviations from ideal performance, and early signs of battery degradation. The designed e-bikes theoretical range was estimated at 67 km, while practical testing demonstrated an extended range of 70.15 km, reflecting a 4.7% improvement. Simulation outcomes were validated against experimental outcomes, yielding deviations of 0.02 and 0.5 V in buck and boost output voltages, 0.015 and 0.333 A in currents, and a SoC deviation of 0.6%. These results confirm the effectiveness of the designed e-bike modelling in electric vehicle applications.
  Keywords: electric vehicles; case study; battery management system; electric bike; converter; experimental validation.
  DOI: 10.1504/IJMMNO.2026.10073645
   
  
• Aggregation and Segmentation for Optimising Load-Bearing Structures  
  by Michael Todinov 
  Abstract: The paper presents a very powerful method in structural engineering, referred to as the method of aggregation, for reducing the weight and increasing load capacity of structures. Rigorous results have been stated and proved, forming the foundation of the aggregation method for structures composed of beams with arbitrarily shaped cross-sections. The essence of this method, overlooked in modern stress analysis, lies in consolidating loaded elements into a reduced number of elements with larger cross-sections, thereby significantly decreasing the material required to support a given total load. For cantilever and simply supported beams, the reduction in material volume, deflection, and stress depends only on the scaling factor of the cross-section along the y-axis and is independent of the scaling factor along the x-axis. The aggregation method was tested by a case study and finite element experiments involving structures built on statically determinate cantilever beams. These studies confirmed that aggregating elements loaded in bending leads to a drastic increase of the load capacity of the structures and a drastic decrease of both the maximum von Mises stress and the maximum deflection.
  Keywords: light-weight design; load capacity; aggregation; segmentation; structural optimisation; cantilever beams; simply loaded beams; bending.
  DOI: 10.1504/IJMMNO.2026.10074638
   
  
• Semi-recursive estimation of multidimensional regression function using the generalised Bernstein polynomial  
  by D.A.N. Njamen, M.A. Issaka, B. Baldagai 
  Abstract: The aim of this article is to use the stochastic approximation method and the generalised Bernstein polynomial to construct an extension of the semi-recursive estimator for the multivariate regression function. We investigate some asymptotic properties of the proposed estimator and determine the optimal parameters that minimise the mean squared error using a cross-validation procedure. Finally, numerical simulation studies show that for these optimal parameters, the proposed estimator has better properties near the edges than kernel-type estimators when the support of the function to be estimated is bounded on at least one side. A real-world application of the proposed estimator on the COVID-19 epidemic in a Chadian hospital showed that the proposed model and the Nadaraya-Watson estimator appear more robust than the kernel-type estimator.
  Keywords: stochastic approximation methods; multivariate regression; Bernstein polynomial; asymptotic properties; cross-validation method; semi-recursivity.
  DOI: 10.1504/IJMMNO.2026.10075411
   
  
• Modelling social media virality with deep learning: insights from Bhopal, India  
  by H.A. Bhavithra, Jebamalar Tamilselvi Jeyaraj, V. Uma Rani, S. Sindu Devi 
  Abstract: Social media platforms, such as WeChat, are central to communication, information sharing, and opinion formation. Understanding how information spreads is vital for combating misinformation, optimising marketing, and enhancing engagement. This study adapts the susceptible-forward-removed (SFR) model, where susceptible users may read messages, forward users share them, and removed users ignore or have already engaged. Mirroring disease transmission, diffusion dynamics are shaped by network structures. Key metrics, including stability thresholds and spread ratios, are analysed alongside sensitivity tests for misinformation control. Numerical simulations via the Runge-Kutta method produce consistent patterns. A recurrent neural network (RNN) is integrated to capture temporal dependencies in SFR counts. Experimental results show RNN outperforms other machine and deep learning models, achieving the lowest MAE (0.020074) and RMSE (0.031919). Accurate SFR prediction provides critical insights into diffusion processes and supports strategic interventions in online communities.
  Keywords: information stability threshold; information spread ratio; sensitivity analysis; Runge-Kutta method; recurrent neural network; RNN; India.
  DOI: 10.1504/IJMMNO.2027.10075551
   
  
• Solving a linear programming problem with greater than equal to constraints using resultant vector ascent method  
  by Subhadip Sarkar 
  Abstract: An iterative method is proposed for minimising a linear programming problem containing all surplus variables, which facilitates the movement from an interior point to the facets through the path traced within the interior of the feasible space while incorporating a resultant vector ascent method and a cutting plane. This approach does not suffer from the looping problem during degeneracy. The proposed vectors, located in the null space of the transformed technological matrix, aid in minimising the problem within the t number (≤m < n) of steps where n and m represent the number of variables and constraints (including the cutting plane). The concepts of elementary column operation and highest cost contribution are administered here to select the appropriate vector in each iteration. This model brings the optimal solution with a worst-case time complexity of O(nm(n m)) which seems faster than the Big-M method, even at n = 2m amidst all positive model coefficients.
  Keywords: linear programming; Big-M Method; resultant vector ascent method.
  DOI: 10.1504/IJMMNO.2026.10075553
   
  
• A modification of weak disposability in non-parametric analysis  
  by Mahnaz Maghbouli, Azam Pourhabib Yekta, Josef Jablonsky 
  Abstract: Data envelopment analysis (DEA) studies in environmental assessment have relied on the comprehensive economic concept of weak disposability as a framework. Dealing with undesirable results in the last decade has often involved using the weak disposability axiom instead of sticking to the idea of free disposability. Having used the axiom of weak disposability enables more accurate efficiency measurement of decision making units (DMUs) while reduces the detrimental effects of undesirable outputs on environment. To effectively tackle with undesirable outputs, this study presents a non-radial model grounded in original technology. By incorporating an abatement factor, this study's key contribution is to redefine a model that accurately measures the reduction of undesirable outputs. Empirical instances reveal the superiority of the proposed model, showcasing its usefulness and enhanced performance compared to its counterpart.
  Keywords: data envelopment analysis; DEA; decision making units; DMUs; abatement factor; undesirable outputs; weak disposability; environmental performance.
  DOI: 10.1504/IJMMNO.2027.10075991
   
  
• Laplace HPM in Caputo and Caputo Fabrizio sense regarding semi analytical solution of fractional-order system of Drinfeld-Sokolov-Wilson equation  
  by Mamta Kapoor 
  Abstract: This paper proposes two innovative semi-analytical solution methods for the fractional Drinfeld-Sokolov-Wilson (DSW) equations, aiming to more efficiently address complex systems characterized by memory effects and anomalous diffusion. This model has several applications in fluid mechanics, plasma physics, and integrable systems. The fractional formulation DSW equation contains essential memory effects and anomalous transport phenomena, which are rarely described by classical integer-order models. To address computational aspects of fractional systems, two novel semi-analytical approaches are proposed: Method I is Laplace transform coupled with the homotopy perturbation method under Caputo derivative and Method II contains same framework under Caputo-Fabrizio derivative. Such methods are selected for their ability to handle fractional operators efficiently without any need of grid discretization. Key results demonstrate that these methods achieve a good compatibility with exact solutions, which is validated through absolute error analysis. Notably, Caputo-based approach consistently outperforms its Caputo-Fabrizio based results (most of the times) in accuracy.
  Keywords: Laplace HPM; Caputo fractional derivative; Caputo Fabrizio fractional derivative; fractional Drinfeld-Sokolov-Wilson equation.
  DOI: 10.1504/IJMMNO.2027.10076094
   
  
• Study of heat conduction process inside the nip of soft calender used in electrode production for Li-ion batteries  
  by Sonali Rangra, Neel Kanth, Jitendra Kumar 
  Abstract: Due to an exponential growing demand of Li-ion batteries, the requirement to enhance and improve the overall performance of the battery is also growing. In an electrode production process, the final step is the calendering process, which greatly impact the overall performance of the Li-ion batteries. Temperature of the bowl, contact time, and nip pressure are the basic parameters of a calendering process, which lead to a change in the mechanical, microstructural, and electrochemical properties of an electrode sheet. This paper aims to determine the effect of basic parameters on the temperature of electrode sheet in the stiffness/thickness direction by utilising Heat Conduction model when a sheet of electrode is passed through the calender nip formed by two bowls having same and different profiles of temperature. The variational iteration method is adopted to approximate the developed model, which evidently depicts the performance and behaviour of the model.
  Keywords: variational iteration method; lithium-ion batteries; electrode production; calendering process; contact time; nip width; thermal diffusivity; porosity; energy density; electric vehicles.
  
  
• Two Methods for Finding Invariant Solutions of Gu  
  by Khristofor V. Yadrikhinskiy, Vladimir E. Fedorov 
  Abstract: We study the fractional Gu
  Keywords: Riemann–Liouville fractional derivative; group analysis; Lie algebra; optimal system of subalgebras; invariant submodel; invariant solution; Lie–Ovsyannikov method; invariant subspace method.
  
  

Special Issue on: Modelling and Optimisation of Power Electronics and Grid Connected Systems for xEV Applications

• Hardware implementation of modular hybrid power generation system architecture with sensor less controller for battery storage applications  
  by Narendra Kumar Muthukuri  
  Abstract: The increasing demand for sustainable and efficient energy systems has led to the development of advanced hybrid power generation (HPG) architectures that integrate renewable sources with battery energy storage. This paper presents a real-time implementation of a modular HPG system combining photovoltaic (PV) energy and a battery energy storage system (BESS), optimised through a high-gain DC-DC converter and a 15-level packed U cell (15PUC) multilevel inverter. A novel sensorless proportional-resonant (SLPR) controller is employed to enhance system stability, reduce harmonic distortion, and ensure compliance with IEEE 519-2014 standards. The proposed system efficiently manages energy flow between PV modules, BESS, and the inverter, enabling reliable standalone and off-grid power delivery. The high-gain converter boosts PV voltage to levels suitable for medium-power applications, while the 15PUC inverter delivers high-quality AC output with reduced switching stress. The SLPR controller ensures dynamic voltage regulation and harmonic mitigation, making the system ideal for residential and commercial energy systems. Simulation and experimental results validate the system’s performance under linear and nonlinear load conditions, demonstrating its potential as a robust and scalable solution for modern energy infrastructure.
  Keywords: MLI; PUC; HG high gain; sensorless proportional resonant; total harmonic distortion; hybrid power generation; HPG; battery storage systems.
  DOI: 10.1504/IJMMNO.2026.10072336
   
  
• Advanced cell-to-cell equalisation strategy for series connected Li-ion batteries in xEV applications  
  by Sridivya Vattem, Srinivasa Rao Gorantla, N. Bharath Kumar  
  Abstract: Lithium-ion batteries are widely used in energy storage systems for electric vehicles (EVs) and renewable applications due to their high energy density and long lifespan However, cell imbalance in battery packs leads to non-uniform states of charge (SoC), degrading performance, reducing cycle life, and posing safety risks such as thermal runaway Traditional passive balancing dissipates excess energy as heat, while active balancing improves efficiency but increases cost and complexity This paper proposes a novel interleaved flyback converter (IFBC) topology for active cell balancing, enabling efficient energy transfer and minimizing heat dissipation with reduced system complexity. A 48V battery pack with 12 series-connected cells using IFBC was developed and tested under charging, discharging, and normal modes. The system maintains a SoC difference below 2% for optimal balancing, achieving balance in 172.5, 352.3, and 238.2 seconds, respectively, across the three modes, demonstrating its effectiveness for practical battery management applications.
  Keywords: active cell balancing; ACB; lithium-ion battery; interleaved flyback converter; IFBC; state of charge; SoC; battery management system; BMS; electric vehicles.
  DOI: 10.1504/IJMMNO.2026.10072339
   
  
• Mathematical modelling and optimisation of electric vehicle battery management using the evolved bat algorithm in grid-connected systems  
  by Batchu Veena Vani, Dharavath Kishan 
  Abstract: The growing need to reduce greenhouse gas emissions and fossil fuel dependence is accelerating interest in electric vehicles (EVs). However, widespread EV adoption faces challenges like prolonged charging times, limited infrastructure, battery degradation, and grid stress. Battery swapping stations (BSS) offer a promising solution to reduce wait times and promote efficient battery usage. This paper proposes an enhanced battery swapping and charging mechanism for EVs using the evolved bat algorithm (EBA), which improves upon the original bat algorithm. The proposed system efficiently schedules battery swaps and charging processes, minimising downtime and optimising energy management in real-time operations. Experimental results demonstrate significant improvements in operational performance, reduced waiting times, and lower computational costs, highlighting the EBA’s potential for revolutionising EV battery management systems.
  Keywords: evolved bat algorithm; EBA; battery charging; battery swapping; electric vehicles.
  DOI: 10.1504/IJMMNO.2026.10072396
   
  
• New energy vehicle strategy for electric vehicle adoption in Asia for sustainable transportation  
  by Rajanand Patnaik Narasipuram, Bindu Vadlamudi, Amit Singh Tandon 
  Abstract: This study presents a comprehensive analysis of the new energy vehicle (EV) market in Asia, focusing on hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV), battery electric vehicles (BEV), and fuel cell electric vehicles (FCEV). High battery costs are identified as a major barrier to adoption. The research emphasises the strategic placement of solar EV charging stations (SEVCS) to address traffic and infrastructure challenges. Mathematical models are used to predict adoption rates, considering factors such as car dimensions, battery warranty, lifespan, and charging infrastructure. The study also evaluates the impact of economic and informational policies, highlighting the importance of collaborative implementation. Standards for EV charging in Asia follow MS IEC 61851 and SAE J1772. The findings offer insights for policymakers to reduce costs, improve infrastructure, and raise consumer awareness, ultimately promoting sustainable transportation and better air quality across the region.
  Keywords: new energy vehicle; NEV; hybrid electric vehicle; HEV; plug-in hybrid electric vehicle; PHEV; battery electric vehicle; BEV; fuel cell electric vehicle; FCEV.
  DOI: 10.1504/IJMMNO.2026.10072603
   
  
• Optimising capacitated EV routing with quantum evolutionary algorithms and federated reinforcement learning  
  by K. Sarangam , Dharani Kumar Chowdary Mirappalli, K. Raghava Rao 
  Abstract: The capacitated electric vehicle routing problem (CEVRP) involves complex interdependencies between dynamic charging and route optimisation. Traditional metaheuristics like genetic algorithms and ant colony optimisation often underperform in large-scale, uncertain EV logistics. This paper presents a quantum-inspired hybrid evolutionary framework (QHIEF) that integrates quantum-inspired evolutionary algorithms (QIEA) with federated reinforcement learning (FRL) for online learning of routing and charging strategies. The problem is modelled as a mixed-integer nonlinear program (MINLP) with stochastic energy consumption and multiple-objective cost minimisation. A refined quantum-inspired differential evolution (QIDE) algorithm enhances route and charging decisions via quantum superposition and adaptive mutation. FRL-based demand forecasting supports real-time vehicle-to-grid (V2G) collaboration. Evaluations on large-scale datasets show a 7.6% reduction in travel distance, 21.3% increase in computational efficiency, and improved convergence over methods like DPCA, CBACO, and SIGALNS, highlighting the frameworks potential for scalable, efficient EV logistics.
  Keywords: quantum-inspired evolutionary algorithm; QIEA; federated learning-based optimisation; capacitated electric vehicle routing problem; CEVRP; grid-connected xEV systems; multi-objective optimisation for power electronics.
  DOI: 10.1504/IJMMNO.2026.10072654
   
  
• Modelling and optimisation of modified non-isolated DC-DC converters using average current mode controller for fuel cell electric vehicles  
  by Chintalapudi K. Krishna, Attuluri R. Vijay Babu 
  Abstract: Conventional proportional-integral (PI) controllers are commonly used in DC-DC converters for fuel cell-powered xEV systems to regulate voltage; however, they exhibit limitations in dynamic performance, particularly under variable load and fluctuating fuel cell outputs, leading to voltage deviations and higher ripple. To address these challenges, this study proposes the implementation of an average current mode (ACM) controller in a single-switch non-isolated DC-DC boost converter. The ACM controller operates by regulating the average inductor current, offering faster transient response, better load regulation, and enhanced stability compared to traditional PI control. Key features of the proposed method include precise voltage tracking, low output ripple, and robustness against input variations, making it highly suitable for real-world xEV applications. Simulation results validate the effectiveness of the ACM approach, maintaining output voltage within 200 V ± 1 V during load changes from 1.25 A to 2.5 A, while the PI controller shows deviations up to ±3 V. This comparative analysis underscores the ACM controllers superiority in ensuring efficient, stable, and high-quality power delivery in fuel cell-based xEV applications.
  Keywords: fuel cell; xEV; voltage regulation; PI controller; ACM controller; dynamic response; ripple reduction.
  DOI: 10.1504/IJMMNO.2026.10072882
   
  
• Advanced control strategies for modelling and optimisation of hybrid microgrid for xEV applications  
  by Ramanjaneya Reddy Nalavala, Bobbillapati Suneetha, Badugu Suresh, Mohammad Mahaboob Pasha 
  Abstract: This work investigates advanced control strategies for integrating renewable energy sources into hybrid microgrids with electric vehicle (EV) integration to enhance voltage quality and minimise harmonic distortion losses. Renewable energy-powered microgrids offer up to 25% improved energy reliability and efficiency but face challenges such as voltage fluctuations and harmonic distortions, which can reach distortion levels of 15% - 20% during peak variability. The inclusion of EVs introduces dynamic load profiles, further impacting grid stability. To address these issues, an adaptive neuro-fuzzy inference system (ANFIS) controller is employed, achieving a 30% reduction in voltage fluctuations and mitigating harmonic distortions by up to 40%. The ANFIS controller leverages neural network adaptability and fuzzy logic interpretability to manage nonlinear and uncertain behaviours in the system. Empirical analysis and simulations demonstrate the effectiveness of the proposed approach in optimising hybrid microgrid performance, supporting renewable energy and EV integration. The findings underscore the significant role of ANFIS controllers in improving grid stability and sustainability, enabling modern hybrid microgrids to achieve a more reliable and efficient energy infrastructure.
  Keywords: adaptive neuro-fuzzy inference system; ANFIS; electric vehicle; EV; harmonic distortion reduction; microgrids; renewable energy integration; voltage quality enhancement.
  DOI: 10.1504/IJMMNO.2026.10073026
   
  
• Adaptive model predictive control strategy for multi-objective optimisation in xEV powertrain and thermal systems across standard drive cycles  
  by Sreedhar Reddy Aemalla, Subbarao Mopidevi 
  Abstract: In electric vehicles (xEVs), conventional energy and thermal management strategies often rely on static or rule-based controls, limiting adaptability to dynamic driving conditions, thermal loads, and battery state-of-charge (SoC). This paper proposes a unified model predictive control (MPC) framework that integrates energy management, regenerative braking, and thermal regulation. The MPC minimises a multi-objective cost function considering temperature deviation, acceleration delay, energy loss, and SoC tracking error. Evaluated over a 24-hour simulation using standard drive cycles (MIDC, UDDS, HWFET, WLTP), the system demonstrates predictive SoC control, adaptive braking, and thermal management that maintains battery temperature within ±5 °C of safe limits while reducing cooling energy by 25%. The proposed MPC extends usable SoC range by 10% and improves motor efficiency to 85%, outperforming traditional methods. Results confirm the framework’s robustness, energy efficiency, and suitability for next generation xEV systems.
  Keywords: model predictive control; MPC; electric vehicles; xEVs; regenerative braking; battery management; thermal regulation; drive cycle optimisation.
  DOI: 10.1504/IJMMNO.2026.10073360
   
  
• Integrated IoT and digital twin architecture for optimising xEV performance and grid interaction  
  by Karanam Amaresh, Jagarapu Satya Venkata Siva Kumar, Mahmad Mustafa, Nathella Munirathanm Giri Kumar, Saleha Tabassum 
  Abstract: Conventional rule-based electric vehicle (EV) systems are constrained by high energy losses, inefficient thermal management, and slower response times. IoT-based monitoring enhances real-time data acquisition, load balancing and energy optimisation but remains limited in predictive control and adaptability. To overcome these challenges, this work proposes an integrated IoT and digital twin (DT) framework that combines real-time monitoring with predictive analytics to improve EV performance. Key operational parameters considered include power generation, battery dynamics, energy consumption, braking efficiency, and response time. Results show improvement in regenerative braking efficiency and an increase in overall cycle efficiency. The IoTDT integration further enhances performance, achieving regenerative braking efficiency of 20%, and prediction accuracy with minimal errors of RMSE = 2.5%, MAE = 1.8%, R² = 0.93. Residual analysis validates model reliability, while response time reduces to 2 ms per cycle, enabling near instantaneous control decisions.
  Keywords: digital twin; EV; energy management; microgrid; internet of things; power electronics converters; predictive analytics.
  DOI: 10.1504/IJMMNO.2026.10073519
   
  
• An adaptive super-twisting sliding mode control strategy for energy and torque optimisation in hydrogen fuel cell electric vehicles  
  by Appalabathula Venkatesh, Simhadri Phani Kumar, Tousif Khan Nizami 
  Abstract: This research introduces a method for power management in hybrid fuel cell-based electric vehicles (HFCBVs) using adaptive super-twisting sliding mode control with dynamic gain (ASTSMC-DG). This enhances efficiency and reliability in energy conversion systems, particularly in bi-directional power converters (BD-PCs) and integrated backup storage systems (IBSSs). The ASTSMC-DG minimises chattering and slow error convergence typically associated with traditional sliding mode control. Simulations show an 8.56% increase in regenerative energy recovery over the PI controller, extending battery life and improving energy efficiency in EVs. The system exhibits lower voltage, current, and torque fluctuations than PI control, with a fuel cell utilisation ratio of 61.2% compared to 72.5% in PI control. This indicates effective power distribution among resources, thereby improving the balance of energy management. A sensitivity analysis confirms the proposed control strategy’s robustness, maintaining efficient energy recovery at approximately 570760 kJ.
  Keywords: hybrid fuel cell-based vehicles; hybrid backup storage system; optimisation of power distribution.
  DOI: 10.1504/IJMMNO.2026.10073644
   
  
• Performance optimisation and torque improvement of reluctance motor using split-winding technique  
  by Izuchukwu Nnanna Eze, Linus Uchechukwu Anih 
  Abstract: The major drawbacks of reluctance machine are majorly low output power and power factor on account of low X_d/X_q ratio. In transfer field machine, this low X_d/X_q ratio is further worsened by the excessive leakage reactance, particularly the quadrature axis reactance. In order to enhance the performance characteristics of the machine, the identical main and auxiliary windings were respectively split on both halves of the machine elements and appropriately interconnected by connecting the main windings to the grid and short-circuiting the auxiliary windings. This scheme reduced the excessive leakage reactance of the machine by half thereby increasing the output power and torque by 100%. Both sets of the machine distributed windings are essentially similar and have equal number of poles. There is also significant improvement in power factor-slip and efficiency-slip characteristics of the machine. The results of the machine by d-q and steady-state analyses are in good agreement.
  Keywords: main winding; auxiliary winding; split-phase; X_d/X_q ratio; half-speed; slip; torque; output power; efficiency; power factor.
  DOI: 10.1504/IJMMNO.2026.10074589
   
  
• Optimised compensation for voltage stability and reliability enhancement in radial distribution systems with electric vehicle integration  
  by Raju B. Sreenivasa , P. Umpathi Reddy, Venkata Narayana Lakamana, Chintalapudi V. Suresh, Mahaboob Shareef Syed 
  Abstract: The rapid growth of electric vehicles (EVs) puts significant pressure on radial distribution systems (RDS), causing voltage instability and reduced reliability. This paper presents an optimised shunt compensation framework combined with stochastic EV load modelling to improve system performance. The method uses the current injection load flow (CILF) technique. It identifies candidate locations using a voltage stability index and refines these through an optimisation-based placement of shunt capacitors. Unlike traditional studies that focus on static loads, this method considers probabilistic EV charging patterns, peak demand clustering, and different penetration levels. Capacitor ratings of 50 kVAr and 100 kVAr are chosen according to IEEE standards and practical utility use. The method is tested on three benchmark IEEE RDS systems (15, 33, and 69 bus) to show scalability. The results demonstrate consistent improvements in voltage stability margin and reliability indices. Even small numerical gains lead to meaningful operational benefits, such as a lower risk of voltage collapse and delayed infrastructure upgrades. The study also explores the role of smart charging, demand response, and vehicle-to-grid (V2G) integration as supportive strategies. The findings confirm that the proposed framework is a practical and scalable solution for maintaining stability and reliability in EV-integrated distribution networks.
  Keywords: radial distribution system; RDS; voltage stability index; VSI; reliability enhancement; electric vehicle; EV; integration; stochastic load modelling; optimised shunt compensation; current injection load flow; CILF; smart charging; vehicle-to-grid; V2G.
  DOI: 10.1504/IJMMNO.2026.10074725
   
  
• Random opposition flow direction algorithm with Lévy distribution for optimum placement of EV fast charging stations and distributed generation  
  by K. Kalyan Kumar , G. Nageswara Reddy 
  Abstract: This article presents an improved flow direction algorithm (FDA) called ROFDA, which integrates Lévy distribution and random opposition-based learning (ROBL) to enhance performance. The Lévy distribution refines the flow velocity vector, enabling more accurate movement toward optimal solutions through flexible and random velocity prediction. ROBL further strengthens the decision-making process by enhancing exploration and convergence. ROFDA is tested on 23 standard benchmark functions and applied to optimise the placement of electric vehicle fast charging stations (EVFCSs) and distributed generation (DG) units within 33- and 69-bus distribution systems (DS). Results show that ROFDA outperforms traditional FDA, OFDA, and MFDA methods. In systems with multiple EVFCSs of varying capacities and locations, it achieves over 70% reduction in power losses in some cases, while maintaining optimal voltage profiles. These improvements confirm ROFDA’s effectiveness for ensuring reliable and efficient power delivery, making it a promising tool for EVFCS and DG planning
  Keywords: electric vehicle charging stations; EVCS; electric vehicles; distributed generation; power loss minimisation; flow direction algorithm; FDA; Lévy distribution; random opposition flow direction algorithm; ROFDA.
  DOI: 10.1504/IJMMNO.2026.10075276
   
  
• Power quality event detection and multi-class classification using MODWT and XGBoost for electric vehicle charging grid  
  by Ganesh Kumar Budumuru, Papia Ray 
  Abstract: The increasing complexity of power quality disturbances (PQDs) in modern power grids, presents significant challenges and needs attention on effective detection and classification algorithms. This study employs a time-series-based maximal overlap discrete wavelet transform (MODWT) for feature extraction. This approach facilitates a detailed analysis of 20 distinct types of power quality disturbances that are generated in the MATLAB/Simulink environment and then applied MODWT to get features such as amplitude, phase, energy, and entropy. After feature extraction, principal component analysis (PCA) is applied for feature selection. The classification model is built using extreme gradient boosting (XGBoost) and dataset is separated such that for testing 20% and for training 80% is utilised. This proposed work introduces hybrid technique capable of effectively detecting PQDs, achieving classification accuracy of 99.92%. This proposed topology demonstrates its effectiveness in power quality enhancement for electric vehicle (xEV) charging infrastructures by improving harmonic distortion and voltage stability.
  Keywords: power quality disturbances; PQDs; maximal overlap discrete wavelet transform; MODWT; extreme gradient boosting; XGBoost; principal component analysis; PCA; random search CV; electric vehicle; xEV.
  DOI: 10.1504/IJMMNO.2026.10075552
   
  
• Techno-economic analysis with energy management strategies of sustainable electrification of a remote village in Western India  
  by Mohd Amir, Imtiaz Ashraf, Fareed Ahmad 
  Abstract: The incorporation of renewable energy sources (RES) into power grids is crucial for ensuring sustainability and economic efficiency in energy systems This study shows Sarmat village in Gujarat, India, the best way to build a hybrid renewable energy system (HRES) with grid connectivity The suggested system integrates solar photovoltaic (SPV), wind turbine (WT), and optional battery energy storage (BES) to tackle the unpredictable characteristics of renewable energy sources The optimisation technique utilises the Giza Pyramids Construction Algorithm, a robust evolutionary approach, to minimise the Levelised Cost of Energy (LCOE) and Total Net Present Cost (TNPC) while ensuring a reliable power supply The techno-economic analysis evaluates various system configurations, including WT/SPV/GRID, GRID/SPV, WT/BES/GRID, & GRID/BES, to determine the most cost-effective solution Results indicate that the SPV/WT/GRID configuration achieves the lowest LCOE of $0 0082/kWh and TNPC of $139,902, making it the optimal choice for energy generation in the region.
  Keywords: renewable energy system; levelised cost of energy; LCOE; rural electrification; energy optimisation; metaheuristic algorithm; India.
  DOI: 10.1504/IJMMNO.2026.10075716
   
  
• An integrated LSTM-based machine learning framework for optimal charging and peak load reduction in xEV-integrated distribution systems  
  by Viswanatha Rao Jawalkar, Ch. Hemanth Kumar, Sudheer Hanumanthakari, K.E. Ch. Vidya Sagar , Venkatakrishnamurthy Talari 
  Abstract: The growing penetration of electric vehicles (xEVs) in distribution networks introduces challenges in peak load management, voltage deviations, and energy cost optimisation. This paper proposes a framework for short-term load forecasting and optimal scheduling in xEV-integrated distribution systems using machine learning. Historical load data, xEV charging patterns, and renewable generation from photovoltaic and wind systems are employed to forecast demand through Long Short-Term Memory (LSTM), Artificial Neural Network (ANN), and Auto-Regressive Integrated Moving Average (ARIMA) models. The forecasts are incorporated into an optimisation scheme for EV charging to minimise the peak-to-average ratio, reduce feeder losses, and maintain voltage stability while ensuring user satisfaction. Simulation results show that LSTM achieves the lowest feeder loss of 471.10 W, outperforming ANN and ARIMA, and reduces peak demand by 18.5% . Comparative analysis highlights LSTM’s superior temporal learning ability for efficient xEV integration.
  Keywords: ARIMA model; artificial neural network; ANN; distribution system optimisation; electric vehicles; load forecasting; long short-term memory; peak load management.
  DOI: 10.1504/IJMMNO.2026.10075871
   
  
• Optimised solar power utilisation for combined water pumping and EV charging in rural areas  
  by RamaKoteswara Rao Alla, Rajani Kandipati , Puvvadi Venkata Mahesh, Ravindranath Tagore Yadlapalli  
  Abstract: To generate clean and sustainable energy rural areas are preferred due to large open fields and ample sunlight. This work proposes a solar-powered system for efficient water pumping and EV charging using smart power management and battery storage. A time-based strategy prioritises irrigation during peak sunlight and EV charging during off-peak hours. A solar water pumping system (SWPS) employing a 3-ϕ induction motor (IM) to pump the water for watering the plants in the fields. Total-cross-tied (T-C-T) PV array configuration has been considered over the traditional PV arrays to enhance the power generation from PV array. The applied control strategy for the SWPS encompasses two primary controllers. One of the controller aims to maximise system output utilising the linear quadratic estimation based Kalman filter-maximum power point tracking (KF-MPPT) algorithm. Meanwhile, the other controller employs six-sector direct torque control (DTC) approach.
  Keywords: direct torque control; incremental conductance; Kalman filter; perturb and observe; water pumping system; two-level inverter; EV charging.
  DOI: 10.1504/IJMMNO.2026.10076010
   
  
• Optimised energy management strategy for fuel cell hybrid electric vehicles using a machine learning framework  
  by Debasis Chatterjee, Chiranjit Sain, Amarjit Roy, Pabitra Kumar Biswas 
  Abstract: Fuel cell hybrid electric vehicles (FCHEVs) are gaining prominence as eco-friendly alternatives to conventional vehicles due to their lower emissions and higher energy efficiency. Effective energy management is crucial for maximising FCHEV performance. Compared with the available literatures in the relevant field, this study proposes a machine leaning (ML) framework, combining artificial neural networks (ANN) and genetic algorithms (GA) to develop an intelligent energy management strategy. The ANN component leverages its predictive and pattern recognition capabilities, while GA optimises network parameters such as weights, biases, and structural hyper parameters to enhance model performance. Additionally, while investigating the real-world driving data, the hybrid ANN-GA model dynamically predicts optimal power distribution between the fuel cell and battery efficiently. Furthermore, the results achieved through different observations confirm that the proposed method outperforms conventional strategies, delivering significant gains in energy efficiency and system responsiveness. Finally, the outcome of the findings demonstrates the superiority of the ANN-GA hybrid model in terms of optimal energy distribution paving the way for more sustainable and efficient transportation systems.
  Keywords: state of charge; SOC; energy management system; EMS; artificial neural network; ANN; genetic algorithm; GA; mean square error; MSE.
  DOI: 10.1504/IJMMNO.2026.10076293
   
  
• Analysis on artificial intelligence based visual perception in autonomous driving systems  
  by Carelin Sibin, Shuvabrata Bandopadhaya, Amarjit Roy, Chiranjit Sain 
  Abstract: Driverless cars have become a major research priority in the automotive industry. Due to the advancement in technology, the development of intelligent transportation systems are accelerated. Autonomous vehicles are able to independently perceive their surroundings and make real-time decisions; however, challenges such as adverse weather, occlusions, and latency still hinder safe deployment. Visual perception forms the foundation of any autonomous driving system (ADS), enabling robust scene understanding and decision-making. This study presents an extensive analysis of three core perception tasks object detection, lane detection, and steering angle prediction using state-of-the-art AI models. It evaluates advanced architectures including CNNs, LSTMs, and transformer-based networks, and benchmarks leading approaches such as YOLOv5, SSD, SCNN, and the NVIDIA steering framework across key datasets, including BDD100K, Udacity, and CARLA. The paper also examines industry case studies, the synergy between AI algorithms and sensor fusion (camera-radar-lidar), and identifies research gaps, emerging trends, and strategic pathways for developing reliable, scalable, and ethically aligned autonomous driving systems.
  Keywords: artificial intelligence; AI; autonomous driving systems; ADS; lane detection; object detection; steering angle prediction; visual perception.
  DOI: 10.1504/IJMMNO.2026.10076294
   
  
• Design, modelling and dynamic performance analysis of a sliding mode controller for LLC resonant converters in xEV charging systems  
  by Chaitanya Kumar Kotamarthi, Attuluri R. Vijay Babu  
  Abstract: The growing need for compact and efficient power conversion in xEV chargers has made the LLC resonant DC-DC converter a preferred choice, thanks to its soft switching behaviour, high efficiency, and low EMI. However, its nonlinear and time varying resonant tank dynamics under wide input and load conditions limit the performance of traditional linear controllers. This paper analyses the converters dynamic characteristics and introduces a sliding mode control (SMC) strategy designed to manage large disturbances, rapid load variations, and parameter uncertainties. A full nonlinear state space model is developed to capture complete switching behaviour, and a Lyapunov-based sliding surface is formulated to guarantee robust, finite time stability. To reduce chattering, a boundary layer approach with a high frequency PWM implementation is used. Simulations show that compared to a PI controller, SMC improves voltage deviation by 10.83%, reduces overshoot by 20%, and shortens settling time by 15%, making it a strong candidate for next generation high power xEV LLC converters.
  Keywords: LLC converter; sliding mode control; SMC; electric vehicle; charger; electromagnetic interference; DC-DC converter.
  DOI: 10.1504/IJMMNO.2026.10076405