International Journal of Mathematical Modelling and Numerical Optimisation (17 papers in press)

Regular Issues

• Sequential quadratic programming-based economic optimisation of an MAP/PH/1 queueing system with negative arrivals and unreliable repairers  
  by Sakshi Pakhrot, D.C. Sharma 
  Abstract: This paper studies an MAP/PH/1 queueing system with negative arrivals, unreliable repairers addressing the challenges posed by repair failures through the analysis of two models. In model 1, a failed repair leads to the removal of all positive arrivals from the system, while in model 2, a standby server is activated to ensure service continuity. The study compares the key performance measures (derived using matrix-analytic method) and economic functions of both models. We have also determined the optimum values of the profit functions for both models using sequential quadratic programming (SQP). The results indicate that model 2 leads to lower loss of positive arrivals and demonstrates greater economic efficiency compared to model 1.
  Keywords: negative arrivals; unreliable repairs; Markovian arrivals; phase-type service; matrix-analytic method; economic optimisation.
  DOI: 10.1504/IJMMNO.2025.10072275
   
  
• A comparative analysis of autonomous and fuzzy non-autonomous SIQR models for infectious disease dynamics  
  by H.A. Bhavithra, S. Sindu Devi 
  Abstract: This study presents a comparative analysis of the classical autonomous SIQR model and a fuzzy non-autonomous SIQR model to simulate infectious disease dynamics under uncertainty. The autonomous model uses fixed parameter, while the fuzzy model incorporates time varying and fuzzy logic-based parameters to capture real world variability. The basic reproduction number is derived for both the models; however, the fuzzy model yields the range of values using membership function for transmission and recovery rates. Sensitivity analysis reveals the fuzzy model is more responsive to uncertainty, with up to 35% fluctuations in outcomes compare to less than 10% in the autonomous model. Incorporating fuzzy viral load results in more nuanced bifurcation patterns and adaptive control strategies. A collocation method is used for numerical simulation over 30 days, showing rapid convergence to disease free state. These findings highlight the fuzzy model potential for informing public strategies, especially during emerging outbreaks with uncertain data.
  Keywords: SIQR model; fuzzy logic; non-autonomous system; bifurcation analysis; fuzzy membership function; epidemic simulation.
  DOI: 10.1504/IJMMNO.2026.10072880
   
  
• Analysis of the impact of the normalisation technique in the CODAS method on supplier selection  
  by Dariusz Kacprzak 
  Abstract: Multi-criteria decision-making methods are applied to various fields of human activity, including the problems of supplier selection A key step in most of these methods is the choice of the technique used to normalise the decision matrix, i.e. the transformation of input data expressed in different units into dimensionless and comparable numerical values This paper analyses the effect of the normalisation technique used on the final values and rankings of alternatives in the combinative distance-based assessment method in a lumber supplier selection problem For this purpose, 12 normalisation techniques are used, each consisting of two formulas applied separately to the benefit-type and cost-type criteria To evaluate these normalisation techniques, a novel use of Hellwig's taxonomic method is proposed, which is then compared with popular measures based on Pearson's correlation coefficient and Spearman's rank correlation coefficient The results obtained confirm the validity of using the linear normalisation maximum-based method in the combinative distance-based assessment method.
  Keywords: CODAS method; Hellwig's taxonomic method; HTM; normalisation technique; objective criteria weights; supplier selection problem.
  DOI: 10.1504/IJMMNO.2026.10073007
   
  
• 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 transmission dynamics of typhoid fever with vaccination, isolation and treatment of infected individuals  
  by Onesmas Tumwekwatse, Mary Nanfuka, Pius Ariho 
  Abstract: In this study, a deterministic mathematical model for typhoid disease transmission with vaccination, isolation, and treatment is developed and analysed. The basic reproduction number R0 is computed and stability analysis is done for both the disease-free and endemic equilibria. The disease-free equilibrium has been found to be locally asymptotically stable whenever R₀ < 1, and the endemic equilibrium is locally asymptotically stable whenever R₀ > 1. The sensitivity analysis computed revealed the parameters that have a great impact on the R₀. Numerical simulations indicate that a combination of vaccination, isolation, and treatment of infected individuals considerably decreases the number of exposed individuals, infected individuals and the bacteria population, whereas the number of individuals isolated for treatment and the recovered individuals first increase and drastically decrease with time. We concluded that implementing all the three control measures is significantly more effective in typhoid fever control and management.
  Keywords: typhoid fever; typhoid transmission; vaccination; isolation; treatment; basic reproduction number; stability analysis.
  DOI: 10.1504/IJMMNO.2026.10073849
   
  
• Lifetime and reliability data modelling via the new type II Topp-Leone-Burr III Poisson distribution  
  by Fastel Chipepa, Wilbert Nkomo, Zhivko Nedev 
  Abstract: In this study, we develop the novel type II Topp-Leone-Burr III-Poisson distribution, a noteworthy contribution to the family of power series distributions. Motivated by the need for more versatile statistical tools, this study aims to develop a highly adaptable lifetime distribution for complex data phenomena. This model demonstrates significant flexibility in effectively modelling skewed data, making it a valuable tool for advanced statistical analysis. We derived some statistical properties of the proposed model. Furthermore, we presented four parameter estimation methods, with the maximum likelihood estimation emerging as the preferred approach due to its faster convergence. To ensure the robustness of these estimates, a Monte Carlo simulation study was conducted, which confirmed that the proposed model consistently delivers reliable parameter estimates. To showcase its practical utility, we provide two real-world examples where this model has been effectively applied.
  Keywords: estimation; Burr III distribution; Poisson distribution; power series; type II Topp-Leone distribution.
  DOI: 10.1504/IJMMNO.2026.10074213
   
  

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
   
  
• 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