Search Results (459)

The rapid transition toward electrified mobility and climate neutrality has prioritized the structural and environmental optimization of battery electric vehicle (BEV) subsystems. While vehicle lightweighting enhances operational efficiency, the production phase of structural enclosures and battery cells frequently introduces severe environmental and economic impacts and supply chain vulnerabilities. This study presents a comprehensive cradle-to-gate environmental life cycle assessment (LCA), life cycle costing (LCC), and semi-quantitative social assessment of alternative battery housing materials and battery cell architectures. To achieve a functionally accurate comparison, alternative materials, including a novel recyclable thermoplastic acrylic sheet molding compound (SMC), commercial thermoset SMCs, aluminum (AlMg3), and stainless steel, are evaluated using an analytical stiffness- and strength-equivalent methodology across three real-world geometric demonstrators. Simultaneously, lithium iron phosphate (LFP) liquid electrolyte prismatic cells and solid-state polymer pouch cells are assessed. Material-level results indicate that, while aluminum minimizes the structural mass, primary aluminum manufacturing exhibits the highest global warming potential and processing costs. Conversely, Polytec SMC and Elium SMC achieve the lowest environmental impacts alongside competitive total production costs. At the cell level, prismatic LFP architectures display superior environmental performance compared to solid-state pouch cells, which suffer from energy-intensive processing and lower volumetric capacity normalization. Demonstrator-level aggregation reveals that the electrochemical cells heavily dominate the environmental and economic footprint of the complete assembly, with the housing accounting for less than 5% of the total global warming potential (GWP) and 1% of the total costs. The social assessment reveals moderate and comparable performance across all systems, with slight advantages for thermoplastic composite-based configurations in terms of circularity potential and innovation perception. Overall, the study highlights the critical importance of the cell architecture and manufacturing processes in determining battery system sustainability, while demonstrating the relevance of lightweight composite housings in reducing the structural mass with a minimal environmental penalty. Full article

Electric logistics vehicles have been increasingly applied in various delivery scenarios due to their environmental benefits. However, limitations in battery capacity and the availability of charging infrastructure remain major obstacles to their wider adoption. This study develops a route optimization model for electric cold-chain logistics vehicles with the objective of minimizing total costs. The model incorporates multiple constraints, including charging mode selection, maximum vehicle capacity, and customer time windows. Computational results show that different charging modes have distinct effects on the total delivery cost of electric cold-chain logistics vehicles. Compared with the public charging mode, the public–private charging mode can achieve lower delivery costs. Moreover, as the difference in unit charging costs between public and private charging stations increases, electric cold-chain logistics vehicles are more likely to utilize charging stations that are farther away but offer lower charging costs. Full article

Active distribution networks (ADNs) require battery energy storage system (BESS) scheduling strategies that reduce operating costs while preserving electrical feasibility and battery lifetime. This paper proposes a parallel adapted JAYA-based methodology for the day-ahead coordinated active and reactive power dispatch of BESS units in this type of grid. The novelty of this research lies in four key contributions: (i) the coordinated optimization of active and reactive power from BESS converters, exploiting their full capabilities for both energy management and voltage support; (ii) the integration of battery degradation costs within the optimization framework, preventing short-term economic strategies that accelerate aging; (iii) the implementation of a parallel adapted JAYA algorithm (AJAYA) with stagnation control and population reactivation mechanisms to enhance solution quality and convergence; and (iv) a comprehensive assessment under both deterministic and uncertainty-based operating conditions, providing a realistic validation of the proposed approach. Our model minimizes conventional generation, DER operation and maintenance, and BESS degradation costs while subject to power balance, distributed energy resource limits, voltage and current constraints, converter capacity, and state of charge (SoC) requirements. Each solution is encoded as BESS active/reactive power setpoints and evaluated through a multi-period AC power flow based on the successive approximations method, including SoC verification and a penalized fitness function. The methodology was validated in modified 33- and 69-node ADNs under deterministic and uncertainty scenarios (based on the conditions observed in Colombia), and it was benchmarked against the population-based genetic algorithm (PGA), the multiverse optimizer (MVO), the salp swarm algorithm (SALPS), the grey wolf optimizer (GWO), and the vortex search algorithm (VSA). According to the results, AJAYA outperformed the comparison methods, providing the best economic performance and exhibiting a robust behavior, with standard deviations below 0.06% and processing times below 0.05 h within a 24-h scheduling horizon. These findings demonstrate that the proposed framework constitutes an AC-feasible and degradation-aware academic contribution and a practical decision-support tool for operators and BESS owners, enabling a cost-effective and reliable BESS scheduling that preserves battery lifetime while improving network operation. Therefore, this research addresses the critical need for advanced energy management strategies that balance short-term economic benefits, technical feasibility, and long-term asset sustainability in modern distribution networks. Full article

Advancements in rechargeable batteries and environmental awareness campaigns have highlighted the importance of electric vehicles (EVs) in recent times. The influx of EVs has posed challenges in almost all areas of technology, including demand-side management (DSM). Extra generating units are switched ON to meet the resultant higher electricity demand, thus reducing the sustainability of the system. To overcome this challenge, effective DSM techniques integrating renewable energy sources are proposed to efficiently utilize the existing generating capacity. The primary goal is to fairly distribute available resources among smart homes and EV owners using the Shapley value and tau value. In this work, two scenarios are examined. First, a community energy storage (CES) approach is adopted to maximize CES revenue, reduce the grid peak-to-average ratio (PAR), and minimize electricity costs. Second, a coordinated group of EVs is utilized to minimize the impact of charging loads during peak hours while concurrently reducing EV charging costs. Simulation results show a reduction in the grid PAR from 2.468 to 1.799, or 27.1%, together with an average reduction of approximately 3% in the electricity cost of participating smart homes. In the EV scenario, optimal scheduling reduces total charging expenditure by 24.8% and lowers the system peak by 2.65% relative to uncoordinated charging of the same fleet, with the total cost distributed among the vehicles by the Shapley value. Benchmarking against a proportional-to-demand rule shows that the tau-value allocation coincides with proportional sharing, whereas the Shapley allocation shifts 4.2% of the allocation away from the household contributing most to the system peak. The framework provides a fair and individually rational cost allocation layer for community-scale peer-to-peer energy markets. Full article

Urban low-altitude bus operations require coordinated eVTOL route decisions under station time windows, dynamic passenger boarding and alighting, load-dependent energy consumption, opportunity charging, and battery safety requirements. We formulate a mixed-integer programming model for reservation-based shared services that lexicographically minimizes the number of deployed aircraft first and total flight distance second while enforcing passenger-capacity, time-window, charging, and battery-safety constraints. To solve large instances efficiently, an Elite-Pool guided Load-Coupled Energy-aware Adaptive Large Neighborhood Search algorithm (EP-LCE-ALNS) is developed by combining forward load-energy-coupled decoding, multi-start construction, elite-route guidance, and adaptive neighborhood search. Benchmark comparisons show that EP-LCE-ALNS consistently achieves strong solution quality across instances of different scales and outperforms the comparison algorithms on large-scale problems. Sensitivity analyses further show that increasing passenger capacity reduces fleet requirements and flight distance, whereas a larger battery safety margin increases both, providing decision support for fleet configuration, route organization, and safety settings. Full article

The rapid development of shared delivery, parcel lockers, and community pickup services has enabled customers to receive orders at multiple alternative service locations. In such scenarios, the fixed-location assumption adopted in traditional electric vehicle routing problems is no longer appropriate. This paper investigates the Electric Vehicle Routing Problem with Time Windows and Flexible Service Locations (EVRPTW-FSL), in which customers can be served at one selected location from a candidate set, while each service location may accommodate multiple customers subject to capacity limits. A mixed-integer optimization model is developed to jointly determine service location assignments, vehicle routing, and charging decisions under vehicle capacity, battery range, partial recharging, and customer time-window constraints. To balance operational efficiency and customer convenience, the objective minimizes the total travel cost and the customer deviation cost incurred when a customer is assigned to an alternative service location rather than the original service location. To solve this NP-hard problem, a Modified Adaptive Large Neighborhood Search with Fix-and-Optimize mechanism (MALNS-FO) is proposed, incorporating specialized operators such as location association destroy, location similarity destroy, and route reconstruction repair, as well as a fix-and-optimize mechanism. Computational experiments demonstrate that the proposed method consistently outperforms benchmark approaches in solution quality and computational efficiency. Results further show that introducing flexible service locations can significantly reduce fleet usage and routing cost by consolidating spatially dispersed demand. Moreover, moderate customer flexibility provides substantial operational benefits while maintaining acceptable service deviation levels. Full article

Urban battery swapping station (BSS) planning is difficult because site opening, service-module allocation, user assignment, battery degradation pressure, travel burden, and congestion are tightly coupled. A plan that minimizes investment alone may create long queues, whereas a plan that only reduces waiting can overbuild costly and underused capacity. This study formulates the urban BSS siting–sizing problem as an operation-aware mixed-integer nonlinear model and evaluates feasible plans with payoff-table global-criterion normalization. To search this rugged planning space, we propose a Stable Portfolio Hyper-Heuristic (SPHH) that combines Greedy construction, BO-guided large-neighborhood search, simulated annealing, and optimized annealing with reheating, followed by feasible-incumbent preservation and non-worsening post-processing. The formal campaign contains 18 synthetic cases, 10 independent repeats, and five algorithms, yielding 900 optimization records. Across the 18 cases, SPHH produced the lowest mean GC score and reduced the mean GC value on average relative to the baseline algorithms. Nonparametric Friedman and Holm-adjusted Wilcoxon tests confirmed statistically significant differences among methods, although the advantage over the strongest baseline was not universal. These results indicate that SPHH is most useful as an offline planning selector that improves recommendation stability when additional computation is acceptable, rather than as a universally faster optimizer. Full article

Energy efficiency remains a major challenge in IoT-enabled wireless sensor networks because sensor nodes operate with limited battery capacity and are often deployed in environments where battery replacement is impractical. Existing clustering and routing protocols frequently optimize cluster-head selection and routing separately, leading to uneven energy consumption, premature node failure, increased routing overhead, and reduced network reliability. This paper proposes an Adaptive Multi-Agent Reinforcement Learning-Assisted Hybrid Bat-Artificial Bee Colony (MARL-BA-ABC) framework for joint cluster-head selection and routing optimization in IoT-enabled wireless sensor networks. The proposed framework combines the Bat Algorithm for local exploitation, the Artificial Bee Colony algorithm for global exploration, and Multi-Agent Reinforcement Learning for adaptive routing. Cluster-head selection and routing are jointly optimized using residual energy, communication distance, traffic load, node density, and link quality. A multi-objective optimization model is formulated to minimize energy consumption, end-to-end delay, routing overhead, and load imbalance while improving packet delivery ratio, residual energy preservation, and network lifetime. Simulation results show that the proposed MARL-BA-ABC framework outperforms Low-Energy Adaptive Clustering Hierarchy (LEACH), Hybrid Energy-Efficient Distributed Clustering (HEED), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Bat Algorithm (BA), and Artificial Bee Colony (ABC), achieving a First Node Death of 2200 rounds, residual energy of 1.32 J, packet delivery ratio of 98.1%, throughput of 410 kbps, and average end-to-end delay of 7.4 ms. Full article

A solar cooling facility with two forms of energy storage, electrochemical (batteries) and thermal (tanks containing the heat transfer fluid, HTF), is simulated. The technical specifications used in the simulation are taken from a recently installed system in an institutional building at the University of Almería (Spain). Electricity generated by photovoltaic (PV) panels is either stored in a battery bank, or supplied directly to the chiller, which is also connected to the grid as a backup. The HTF circulates through a storage tank and is driven to a heat exchanger to cover the refrigeration demand. The whole system is modeled in TRNSYS with the corresponding meteorological data: the PV array has a peak power of 23.4 kW, the compression chiller power is 70 kWt, and the building demands of refrigeration from high and low season amount to 413.5 kWh/day and 62.8 kWh/day, respectively. The battery bank capacity is 40.8 kWh and the tank has a volume of 4000 L. Different configurations of energy storage (only electrical, only thermal, and hybrid) are tested for both demands. The results show that the refrigeration needs can be covered with solar energy and storage in the low season, with a surplus that can be driven to the building. In the high demand season, an extra input from the grid is needed as the current facility covers 89.3% of the total electricity requirements per day (54.5% if no storage is used). Finally, the system performance is evaluated over an intermediate-demand month. Overall, it is found that electrical consumption from the grid is minimal when batteries are used, either alone or in conjunction with thermal storage. Full article

A three-dimensional structural model of a cylindrical lithium-ion battery pack incorporating composite phase change material (PCM) is developed in this study, and numerical simulations are conducted using CFD software to investigate the heat dissipation characteristics of the battery pack. The results show that the composite PCM effectively suppresses the temperature rise within the battery pack, maintaining both the temperature and the temperature difference in the battery pack within acceptable ranges. Parameter analysis reveals that increasing the radial thermal conductivity of the battery reduces the heating rate and improves the temperature uniformity of the overall system. Within the investigated parameter range, increasing the thermal conductivity of the composite PCM beyond approximately 1 W/(m·K) results in a region of diminishing improvement in thermal performance. Beyond this range, further increases in thermal conductivity result in only marginal reductions in the maximum temperature, indicating that excessive enhancement of thermal conductivity provides limited thermal benefits and should be balanced with latent heat capacity. An increase in the latent heat of the composite PCM lowers both the maximum temperature and the maximum temperature difference at the end of discharge, thereby enhancing system temperature uniformity. Conversely, enlarging the external air convection heat transfer coefficient yields a limited cooling effect while deteriorating the temperature uniformity within the system. Therefore, on the principle of fully utilizing latent heat and minimizing energy consumption, the external convection heat transfer coefficient should be set as low as possible. This study provides theoretical guidance for the parametric design of PCM-based thermal management systems. Full article

This study presents a mathematical programming approach for the optimal design of a renewable energy system in a grid-connected public building, incorporating green hydrogen production for surplus energy storage. The system includes wind turbines, solar panels, batteries, a hydrogen unit, and a grid connection. Hydrogen can also be sold as vehicle fuel, generating revenue and reducing the environmental impact. Unlike traditional hydrogen smart grid models that rely on continuous capacity variables—which often yield non-commercial fractional unit sizes—our MILP framework strictly enforces discrete equipment capacities matching real-world procurement specifications. The methodology is applied to the Chemical Engineering Department Building at the University of Patras, Greece, with two objectives: minimizing annual cost and minimizing carbon dioxide emissions. While higher grid electricity tariffs increase absolute total energy costs, they significantly enhance the economic competitiveness and payback of local renewable energy and green hydrogen installations, shifting the optimal system configuration toward self-sufficiency and deep decarbonization. Emission minimization achieves substantial reductions with acceptable economic trade-offs, mainly through hydrogen replacing fossil fuels in transport. A GAMS-based model demonstrates that integrating renewables and hydrogen storage can enhance energy security, lower costs, and reduce the environmental impact in public buildings. Full article

Supplying shore-side electricity to ships at berth, a practice known as cold ironing, removes the emissions of their auxiliary engines, yet the resulting electricity demand is large, highly seasonal and hard to serve economically from the grid at the small and medium-sized ports that make up most of the European network. This study asks how to meet that demand affordably and cleanly. It develops a smart-sizing and energy-management framework for a grid-connected hybrid renewable energy system that jointly optimizes solar photovoltaic and wind capacity together with a combined battery-and-hydrogen storage envelope. An energy-conserving stochastic reconstruction of the hourly cold-ironing demand is embedded within a genetic algorithm that minimizes the levelized cost of energy and the carbon footprint, and the system is operated by a transparent, priority-based controller. On a full year of real operational data from a Mediterranean port, the optimizer selects 380 kilowatts of photovoltaic capacity and a 2064 kilowatt-hour, battery-dominated storage envelope, reaching a renewable penetration equal to 76 percent of annual demand, with 57 percent of demand met without the grid. Relative to grid-only cold ironing it lowers the levelized cost of energy by about 10 percent on a screening basis, before life-cycle costs bring it to roughly grid parity, while cutting greenhouse-gas emissions by 45 percent; emissions fall 72 percent relative to auxiliary engines. Storage capacity, not oversized renewable generation, proves decisive for deep decarbonization, and battery storage dominates the cost-optimal design for this diurnal load. The framework gives port operators a transferable, data-driven decision-support tool. Full article

This paper considers an optimal energy management problem for a real electric vehicle charging system with photovoltaic power generation, stationary battery energy storage, and an electrical grid. A linear optimization model is formulated with the objective of minimizing the cost of electricity purchased from the grid while satisfying the technical constraints of system components and the energy requirements of electric vehicles. Various parameters of the system are evaluated when changing the main variables. From the analysis and comparison of the results, a conclusion is drawn about resource savings with appropriate management of energy capacities. Full article

While traditional microgrid optimization focuses primarily on minimizing Net Present Cost (NPC), real-world deployment success depends on multiple operational factors, including system resilience, capacity headroom, and battery health preservation. This paper presents an enhanced Particle Swarm Optimization (PSO) framework coupled with a Multi-Design Optimization (MDO) methodology that incorporates use-case objective functions that take into account several metrics beyond pure economics. Moreover, the enhanced PSO algorithm allowed us to evaluate near-optimal solutions that can help with plant design with constrained industrial choices. The proposed approach extends the established PSO-MDO method by introducing a customized cost function that simultaneously evaluates: (i) economic viability through NPC, (ii) system resilience through capacity factor and headroom utilization, and (iii) battery longevity through Depth of Discharge (DoD) and C-rate constraints. We apply this methodology to the Areza microgrid in Eritrea, a remote PV-BESS-diesel hybrid system that has been operational since 2019, with two primary objectives: first, quantifying design differences between optimization-derived and as-built configurations, and second, assessing system resilience under load growth scenarios. The results demonstrate that the objective function reveals critical trade-offs invisible to traditional NPC-only optimization, particularly regarding battery stress patterns and system capacity margins. The results reveal a broad near-optimal range where NPC changes by about 5%, but CAPEX varies by almost 28%. This indicates how very different combinations of components can achieve similar lifecycle costs. The best-performing design achieves an NPC ≈ EUR 2.02 M and an LCOE ≈ EUR 0.161/kWh, with sizing around PV 973 kW, battery 2236 kWh, inverter 392 kW, and generator 522 kW. The study highlights key trade-offs: reducing diesel use often leads to more curtailment, and systems that rely more on diesel are more sensitive to demand growth. Smaller systems face higher fuel costs when demand rises by 20%. Full article

State of Health (SOH) is a critical metric for evaluating the efficient and reliable operation of lithium-ion batteries (LIBs), although it cannot be directly measured. Accurate SOH prediction throughout the entire lifecycle of LIBs remains a significant challenge, primarily due to severe signal fluctuations and complex degradation mechanisms. In this paper, a novel hybrid Transformer-based architecture for SOH prediction is introduced, termed KF–SAMformer–GRU, which integrates a Kalman filter (KF) optimizer, a sharpness-aware minimization Transformer (SAMformer) model, and a multi-layer gated recurrent unit (GRU). To overcome the challenges of multivariate long-term forecasting, we innovatively integrate SAMformer to extract robust feature indicators, actively mitigating data distribution shifts via sharpness-aware minimization. Furthermore, reversible instance normalization (RevIN) is first introduced to tackle non-stationarity in multi-source datasets, effectively eliminating uncertainty and significantly boosting generalization capability. Complementing this, the KF mechanism is uniquely employed to fuse multi-dimensional features, reducing computational overhead while accelerating training. Finally, the multi-layer GRU precisely refines the mapping between SOH metrics and predicted values. Experimental validation on the NASA and CALCE datasets demonstrates the superiority of our approach, achieving a MAPE of 0.01, an RMSE of 0.91%, and an R² of 0.99. Notably, the method accurately captures phenomena such as battery capacity regeneration, exhibiting superior performance at peaks and valleys while maintaining high computational efficiency. Full article