Search Results (91)

Although greenhouse vegetable production is rapidly shifting toward innovative soilless systems, soil-based conventional cultivation still dominates globally. This production system faces growing pressure to transition to sustainable practices. However, introducing biofertilisers into intensive systems often yields inconsistent results. Specifically, their effects on different lettuce traits vary due to complex relationships between genotype, biofertiliser, environmental conditions, and market demands. Single-parameter evaluations fail to balance conflicting criteria, necessitating multi-criteria decision-making (MCDM) methods for selecting optimal choices. This study aims to overcome these inconsistencies through an integrated fuzzy MCDM-based optimisation model. Three lettuce cultivars (‘Carmesi’, ‘Aquino’, and ‘Gaugin’) were grown in an unheated Surčin (Serbia) greenhouse during a 58-day autumn experiment using a complete block design. Four treatments were applied: a control (without fertilisation), effective microorganisms, a Trichoderma-based fertiliser, and their combination. Biofertilisers were applied before transplanting and four times foliarly during the vegetation period via battery sprayer. This defined 12 production models (cultivar–fertiliser pairs), evaluated across 10 criteria: agronomic (core ratio, number of leaves), quality (nitrate content, total antioxidant capacity, total soluble solids, and chlorogenic acid), sensory (overall taste, overall quality), and economic (total variable costs, total income). Four decision-making experts from the Faculty of Agriculture and the ready-to-eat salad industry assessed weighting coefficients using the fuzzy PIPRECIA (PIvot Pairwise RElative Criteria Importance Assessment) method. The fuzzy MARCOS (Measurement Alternatives and Ranking according to COmpromise Solution) method was used to rank the alternatives. To confirm the stability of the obtained ranking with the fuzzy MARCOS method, we performed sensitivity analysis through 20 different scenarios. Applied fuzzy methods identified alternative A11—‘Aquino’ cultivar with combined biofertilisers—as the best-ranked option, followed by A6 and A7. This study validates fuzzy PIPRECIA and fuzzy MARCOS as effective tools for optimising lettuce production models. They support farmers in selecting the most favourable solution based on multiple criteria, aiding the shift from mineral fertilisers to sustainable biofertiliser-based systems in intensive production—especially helpful for producers making this transition. Full article

The rapid increase in end-of-life lithium-ion batteries demands sustainable recycling routes for lithium recovery. This work presents a novel integrated hydrometallurgical–electrodialysis process designed specifically for recovering lithium from off-specification NMC cathode materials while enabling full reagent recyclability. Selective leaching with oxalic acid was optimised by setting the water-to-oxalic acid dihydrate ratio (H₂O/OA·2H₂O) to 7.3:1 w/w, achieving 81% lithium extraction at room temperature within 2 h while limiting the co-dissolution of Ni, Co and Mn to 0.2%, 1.6% and 1.7% by weight, respectively. The resulting leachate was processed in a four-chamber electrodialysis cell equipped with two Nafion 117 cation-exchange membranes and one Neosepta AMX-fmg anion-exchange membrane operating at −1.6 V versus Ag/AgCl, enabling 96% lithium recovery and 98% oxalic acid recovery. The regenerated oxalic acid stream (41.8 g L⁻¹) was fully restored to its initial concentration and reused in successive cycles without performance loss. Subsequent precipitation of lithium with Na₂CO₃ yielded 99.3%-pure Li₂CO₃. This combined leaching–electrodialysis–precipitation presents a high selectivity, low-waste, circular recovery system, offering a scientifically original approach that integrates reagent regeneration with high-purity lithium production. Full article

The widespread adoption of solar energy storage systems is transforming the global energy landscape, enabling more efficient use of renewable resources and enhancing energy resilience. The integration of residential batteries significantly enhances energy efficiency and sustainability by facilitating the storage of surplus renewable energy, providing reliable backup during power outages, and optimising energy consumption. This study explores the factors influencing end-user adoption of batteries, utilising the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) as a guiding framework to analyse adoption behaviours and determinants. This study employs a qualitative approach using semi-structured interviews with stakeholders divided into three categories: regulatory authorities, industry experts, and end-users. This study highlights key factors influencing battery adoption, such as energy independence, grid reliability, and environmental impact, while addressing challenges like regulatory inconsistencies and installer training. Study extends UTAUT2 to residential battery adoption, emphasising performance expectancy, facilitating conditions, and price value in decision-making and makes a methodological contribution by validating deeper qualitative insights into renewable technology adoption. The practical implications emphasise the need for designing targeted policies, such as subsidies and net metering, alongside developing user-centric systems that enhance affordability, usability, and consumer awareness to facilitate residential battery adoption. Full article

Reliable prediction of energy consumption for electric motorcycles in sub-Saharan Africa requires models that reflect local riding conditions and measured component behaviour. This paper presents a validated, physics-based simulator for the Roam Air electric motorcycle that combines longitudinal dynamics with empirically derived motor and inverter efficiency maps obtained from dynamometer testing. The model ingests measured drive cycles and elevation-derived gradients to compute tractive effort and battery power flow and is validated against six real-world city and highway trips in Nairobi. The simulator reproduces temporal battery-power profiles with strong correlations between 0.87 and 0.91 and predicts energy per distance with small positive bias, achieving errors between 0.4% and 11.3%, where the measured energy consumption per distance ranges between 30.2 and 51.7 Wh/km. A sensitivity analysis quantifies the influence of key design parameters, and a scenario analysis assesses the impact of representative African driving conditions, including terrain, posture, payload, and surface type. The resulting framework is compact, transparent, and potentially adaptable to a wide range of electric two-wheelers, supporting design optimisation and electrification planning in the region. Full article

Renewable Energy Communities are expected to play a key role in the decarbonization of power systems, but their design and operation involve multiple, often conflicting objectives and evolving regulatory frameworks. However, prospective REC promoters and members must make early-stage design choices under policy constraints while balancing economic, environmental, and reliability goals, which motivates the need for transparent and reproducible decision-support tools. This paper presents Adapters, a two-level decision-making tool that couples long-term planning with short-term operational adaptation for hybrid renewable energy systems. The core optimisation model is explicitly multi-objective, with three weighted terms (w₁, w₂, and w₃) that represent total cost, CO₂ emissions, and unserved energy, respectively, allowing users to explore trade-offs between economic performance, environmental impact, and reliability. The tool integrates detailed component models (such as photovoltaic, wind, and battery storage) with a flexible optimisation layer and architecture compatible with digital-twin approaches. Its capabilities are illustrated through prototype single-household case studies, showing how different stakeholder preferences and regulatory conditions can be reflected in the choice of objective weights and system configurations. The overall aim is to provide a transparent and reproducible environment to support the emergence and operation of RECs in line with EU energy and climate goals. Full article

This paper presents the design and optimisation of a low-power embedded sensor-node architecture for real-time environmental monitoring with on-board machine-learning inference. The proposed system integrates heterogeneous sensing elements for air quality and ambient parameters (temperature, humidity, gas concentration, and particulate matter) into a modular embedded platform based on a low-power microcontroller coupled with an energy-efficient neural inference accelerator. The design emphasises end-to-end energy optimisation through adaptive duty-cycling, hierarchical power domains, and edge-level data reduction. The embedded machine-learning layer performs lightweight event/anomaly detection via on-device multi-class classification (normal/anomalous/critical) using quantised neural models in fixed-point arithmetic. A comprehensive system-level analysis, performed via MATLAB Simulink simulations, evaluates inference accuracy, latency, and energy consumption under realistic environmental conditions. Results indicate that the proposed node achieves 94% inference accuracy, 0.87 ms latency, and an average power consumption of approximately 2.9 mWh, enabling energy-autonomous operation with hybrid solar–battery harvesting. The adaptive LoRaWAN communication strategy further reduces data transmissions by ≈88% relative to periodic reporting. The results indicate that on-device inference can reduce network traffic while maintaining reliable event detection under the evaluated operating conditions. The proposed architecture is intended to support energy-efficient environmental sensing deployments in smart-city and climate-monitoring contexts. Full article

Magnesium–sulfur (Mg-S) batteries represent a novel category of multivalent energy storage systems, characterised by enhanced theoretical energy density, material availability, and ecological compatibility. Notwithstanding these benefits, the practical implementation of this approach continues to be hindered by ongoing issues, such as polysulfide shuttle effects, slow Mg²⁺ transport, and significant interfacial instability. This study emphasises recent progress in utilising transition metal chalcogenides (TMCs) as cathode materials and modifiers to overcome these challenges. We assess the structural, electrical, and catalytic characteristics of TMCs such as MoS₂, CoSe₂, WS₂, and TiS₂, highlighting their contributions to improving redox kinetics, retaining polysulfides, and enabling reversible Mg²⁺ intercalation. The review synthesises results from experimental and theoretical studies to offer a thorough comprehension of structure–function interactions. Particular emphasis is placed on morphological engineering, modulation of electronic conductivity, and techniques for surface functionalisation. Furthermore, we examine insights from density functional theory (DFT) simulations that corroborate the observed enhancements in electrochemical performance and offer predictive direction for material optimisation. This paper delineates nascent opportunities in Artificial Intelligence (AI)-enhanced materials discovery and hybrid system design, proposing future trajectories to realise the potential of TMC-based Mg-S battery systems fully. Full article

Electrospinning has emerged as a powerful technique for fabricating customised nanofibrous materials with integrated functional nanostructures, offering significant advantages for electrochemical energy applications. This review highlights recent advances in using electrospun nanofibres directly as active components in devices such as batteries, supercapacitors, and fuel cells. The emphasis is on the role of composite design, fibre morphology and surface chemistry in enhancing charge transport, catalytic activity and structural stability. Integrating carbon-based frameworks, conductive polymers, and inorganic nanostructures into electrospun matrices enables multifunctional behaviour and improves device performance. The resulting nanofibrous composite materials, often after heat treatment, can be used directly as electrodes or self-supporting layers, eliminating the need for additional processing steps such as size reduction or preparation of slurries and inks for creating functional nanofibre-based deposits. The importance of composite nanofibres as an emerging strategy for overcoming challenges related to scalability, long-term durability, and interface optimisation is also discussed. This review summarises the key results obtained to date and highlights the potential of electrospun nanofibres as scalable, high-performance materials for next-generation energy technologies, outlining future directions for their rational design and integration. Full article

Raw material price volatility, freight rates, and foreign exchange all pose significant uncertainty for lithium-ion battery manufacturers, jeopardising procurement planning and financial stability. In this paper, we formulate a discrete Conditional Value-at-Risk (CVaR) optimisation model to design implementable robust hedging strategies for multi-factor cost exposure. Unlike conventional continuous hedge models, which are often severely parameter-sensitive and require frequent rebalancing, the discrete approach takes hedge ratios to be fixed at a finite implementable grid (0%, 50%, 100%) and simultaneously minimises the expected cost and tail risk. We conduct two case studies: the first evaluates the model behaviour under stochastic price shocks using a multi-market simulation data set, and the second subjects the model to stress testing on correlation drift and tail amplification in order to examine systemic robustness. Our results show that, compared with an OLS-based hedge or a fully hedged benchmark, the discrete CVaR framework yields smoother hedge patterns, lower tail losses, and improved liquidity stability; in addition, our results indicate that, when combined with tail-risk penalisation, decision discretisation can endogenously confer robustness to the industrial procurement horizon. This work contributes to the stochastic optimisation literature and provides a practical tool for mitigating volatility in the global lithium supply chain. Full article

The increasing electrification of mobility within smart cities has accelerated the need for intelligent energy management strategies that jointly address cost, emissions, and battery health. This study develops a health-aware hybrid reinforcement–predictive energy manager (H-RPEM) designed for photovoltaic–electric vehicle (PV-EV) microgrids. The proposed controller unifies model-based predictive optimisation with adaptive reinforcement learning to achieve both short-term operational efficiency and long-term asset preservation. A comprehensive dataset of solar generation, EV charging behaviour, and stochastic load profiles was employed to train and validate the hybrid control framework under realistic operating conditions. Quantitative results indicate that the proposed H-RPEM controller achieves an 18.7% reduction in total operating cost and a 22.5% decrease in carbon emissions, whilst maintaining the battery state-of-health above 0.95 throughout a 24 h operational cycle. When benchmarked against standard predictive control, the hybrid strategy converges 30–40 episodes faster and delivers a 25% improvement in reward stability, demonstrating enhanced robustness and learning efficiency. The results confirm that H-RPEM achieves robust and balanced performance across economic, environmental, and technical domains, establishing it as a scalable and health-conscious control solution for next-generation smart city microgrids. Full article

The miniaturisation of electronic devices has intensified the demand for compact, high-performance lithium-ion batteries. This review synthesises recent progress in microscale battery development, focusing on microfabrication techniques, nanostructured materials, porosity-engineered architectures, and strategies for reducing non-active components. It explores both top–down and bottom–up fabrication methods, the integration of nanomaterials, the role of gradient electrode architectures in enhancing ion transport and energy density, along with strategies to reduce non-active components, such as separators and current collectors, to maximise volumetric efficiency. Advances in top–down and bottom–up fabrication methods, including photolithography, laser structuring, screen printing, spray coating, mechanical structuring, and 3D printing, enable precise control over electrode geometry and enhance ion transport and material utilisation. Nanostructured anodes, cathodes, electrolytes, and separators further improve conductivity, mechanical stability, and cycling performance. Gradient porosity designs optimise ion distribution in thick electrodes, while innovations in ultra-thin separators and lightweight current collectors support higher energy density. Remaining challenges relate to scalability, mechanical robustness, and long-term stability, especially in fully integrated micro-battery architectures. Future development will rely on hybrid fabrication methods, advanced material compatibility, and data-driven optimisation to bridge laboratory innovations with practical applications. By integrating microfabrication and nanoscale engineering, next-generation LIBs can deliver high energy density and long operational lifetimes for miniaturised and flexible electronic systems. Full article

Despite growing interest in positive-energy and net-zero-energy buildings (NZEBs), few studies have addressed the integration of biobased construction with building-integrated photovoltaics (BIPV) under hot–dry climate conditions, particularly in Morocco and North Africa. This study fills this gap by presenting a simulation-based evaluation of energy performance and renewable energy integration strategies for a residential building in the Fes-Meknes region. Two structural configurations were compared using dynamic energy simulations in DesignBuilder/EnergyPlus, that is, a conventional concrete brick model and an eco-constructed alternative based on biobased wooden materials. Thus, the wooden construction reduced annual energy consumption by 33.3% and operational CO₂ emissions by 50% due to enhanced thermal insulation and moisture-regulating properties. Then multiple configurations of the solar energy systems were analysed, and an optimal hybrid off-grid hybrid system combining rooftop photovoltaic, BIPV, and lithium-ion battery storage achieved a 100% renewable energy fraction with an annual output of 12,390 kWh. While the system incurs a higher net present cost of $45,708 USD, it ensures full grid independence, lowers the electricity cost to $0.70/kWh, and improves occupant comfort. The novelty of this work lies in its integrated approach, which combines biobased construction, lifecycle-informed energy modelling, and HOMER-optimised PV/BIPV systems tailored to a hot, dry climate. The study provides a replicable framework for designing NZEBs in Morocco and similar arid regions, supporting the low-carbon transition and informing policy, planning, and sustainable construction strategies. Full article

The transition to electric aircraft for zero-emission transport requires integrating thermal management systems for high-performance batteries without incurring significant weight, balance, or aerodynamic penalties. This study focuses on the aerodynamic penalties associated with air-cooling systems that can compound the presently unavoidable reduction in endurance imposed by current battery energy density limitations. Building on previous research into battery installation layouts and internal cooling flows, this study is the first to investigate the lift-to-drag (L/D) optimisation for the multiple wing-mounted inlets and outlets necessary for air-cooling batteries in the wing of an electrified aircraft. Wing leading-edge inlets and NACA (National Advisory Committee for Aeronautics) ducts were analysed by systematically varying their layout, number, and dimensions. The analysis evaluated their effects on the wing’s lift, drag, and moment to maximise the L/D. Multiple highly efficient simulation test designs were developed to screen for the main factors to identify the best inlet and outlet configuration, resulting in 66 different Computational Fluid Dynamics (CFD) simulations in Ansys Fluent. Following this, three CFD verification cases of the best configuration were conducted to verify the cooling effect by combining both internal and external flow simulations with heat generation. Compared to the baseline wing of the carbon combustion aircraft, the best configuration caused a 1.75% reduction in L/D, range, and endurance. While the aerodynamic penalty is now minimised, the internal battery pack layout requires further optimisation to re-establish uniform cooling across the battery pack. Designers may still be able to separate the CFD analysis of the internal and external flow regimes with idealised inlets and outlets; however, more whole-field CFD iterations are needed to guide such subdivision to a viable and safe design for wing-mounted batteries. Further, the margins are such that wing-mounted electrification warrants careful instrumented validation in an aircraft. These findings provide crucial design guidance for sustainable aviation, particularly to enable after-market electrification projects. Full article

The storage of different forms of energy is becoming increasingly important in the energy system sector, due to the significant fluctuations that renewable energy sources influence on urban energy systems. Nowadays, these sources have been promoted for the transition towards modern energy systems at different scales, due to their reduced emissions of greenhouse gases. Yet, many doubts remain about their efficacy in urban settlements worldwide. For this reason, to promote the fast implementation of renewable energy technologies around the world, it is of great importance to design and develop free-access and user-friendly tools to help stakeholders in the planning and management of urban energy districts. The present study has proposed an evaluation tool to model decentralised energy storage systems using Urban Building Energy Models, including an optimisation method to size the best capacity in each building of a district. The developed models simulate two storage technologies: battery power banks and heated water tanks. To present the outcomes of the tool, these models have been tested in two scenarios of Portugal, located in a densely populated area and the most isolated region of the country. Among the most important findings of the results are their ability to evaluate the performance of individual buildings by group archetype and total district metrics, using different temporal periods in a single model to identify the buildings taking most advantage of storage technologies. In addition, the optimisation algorithm efficiently estimated the ideal size of each storage technology, reducing the need of unnecessary capacity. Full article

This paper investigates the feasibility of vehicle-integrated photovoltaic (VIPV) systems for light vehicles by developing and simulating an intelligent solar integration design based on the Tesla Model 3. The proposed system incorporates roof and bonnet-mounted photovoltaic modules, each managed by independent buck converters employing maximum power point tracking (MPPT) for optimal energy extraction. A novel fuzzy logic controller was designed to dynamically allocate auxiliary battery charging between the traction battery and the solar subsystem, using real-time irradiance and state-of-charge (SOC) inputs. The system was implemented in MATLAB/Simulink with location-specific data for Melbourne, Australia. Simulation results demonstrate high converter efficiencies of 94–95%, stable MPPT convergence within 0.5 s and an estimated annual solar contribution of 930 kWh, confirming effective control and energy management under varying conditions. This work highlights the innovative application of adaptive fuzzy control and dual MPPT coordination within VIPV systems and provides a validated basis for future optimisation and real-world integration. Full article