Search Results (7,975)

Driven by demanding global emission regulations and the urgent requirements for sustainable mobility, Electric Vehicles (EVs) have emerged as the primary alternative to Internal Combustion Engine (ICE) vehicles. Central to this transition is the electric propulsion system (EPS), a multidisciplinary integration of power electronics, advanced motor drives, and electrochemical energy storage. This paper provides a comprehensive overview of the current landscape of power electronic drives, focusing on the evolution of high-efficiency traction motors and next-generation energy storage systems (ESSs), and advancements in ultra-fast chargers. The analysis explores the vital impact of power converters, evaluating recent breakthroughs in wide-bandgap (WBG) semiconductors and advanced control topologies that enhance energy density and thermal management. Furthermore, the study identifies critical challenges in the design, modulation, and operational reliability of converters under dynamic automotive environments. By synthesizing current research trends and technical bottlenecks, this paper offers insights into the future trajectory of power electronics in achieving high-performance, cost-effective, and carbon-neutral transportation. Full article

The emergence of citizen energy communities under the European Clean Energy Package is creating new opportunities for neighboring households to collectively reduce electricity costs through local energy sharing. This paper presents a distributed multi-agent energy management system for a two-household residential energy community in which each household is equipped with photovoltaic generation and a battery energy storage system operating under realistic hourly-varying electricity prices. Each household is managed by an independent Deep Q-Learning agent that learns a cost-optimal charging and discharging policy using only local observations. In parallel, a coordination agent, implemented on the SPADE platform with XMPP-based messaging, oversees real-time peer-to-peer energy transfers between households, enabling energy exchange whenever one household has surplus generation and another faces a deficit. The two households are deliberately configured with complementary profiles: one has higher PV generation capacity while the other has higher energy consumption. This setup creates natural opportunities for local energy sharing between them. Performance is assessed through a three-level evaluation framework: (i) individual household economics (cost reduction, battery management, grid exchanges), (ii) coordination efficiency (transfer frequency, direction, and volume), and (iii) aggregate community performance, which isolates the added value of peer-to-peer sharing beyond what each household achieves through individual BESS optimization. Numerical experiments using GEFCom2014 solar generation data, synthetic residential load profiles calibrated following documented consumption patterns, and day-ahead price signals representative of the Spanish electricity market demonstrate that both Deep Q-Learning agents independently learn effective charge/discharge strategies aligned with price signals and PV availability. They also show that the coordination layer further reduces community grid dependence by routing surplus energy locally rather than exchanging it with the main grid at less favorable rates. The results confirm that a well-engineered integration of decentralized reinforcement learning with a lightweight coordination protocol can deliver measurable economic benefits in realistic residential energy communities without requiring centralized training, shared data, or complex multi-agent reinforcement learning architectures. Full article

This paper presents a practical and communication-independent energy management system (EMS) for a DC microgrid supply within the firezone of a cruise ship. The proposed approach prioritizes operational reliability and fault tolerance under emergency conditions, where communication availability and control complexity should be minimized. The proposed DC microgrid integrates photovoltaic systems (PVs), fuel cell systems (FCs), and lithium-iron-phosphate (LFP) battery energy storage systems (BESSs), coordinated through a rule-based EMS combined with droop-controlled converters. The electrical topology considered in this study is a collaborative development of the project consortium of the publicly funded project Sustainable DC Systems (SuSy), featuring a novel configuration with two independent horizontal busbars for the Cabin Area Distribution (CAD) and Technical Area Distribution (TAD). The EMS can manage two operational scenarios: (i) regular operation, with two decentralized droop controls where power generation is distributed among all generators based on their respective capacities, and a power curtailment strategy is applied to prevent overcharging of BESSs; and (ii) irregular operation, where a fault on one of the vertical busbars triggers the use of reserved battery storage capacity on both sides of the ship and activates load-shedding to ensure continued operation of critical loads and sustain grid functionality. The effectiveness of the proposed architecture is validated through detailed MATLAB/Simulink simulations. Under regular conditions, the EMS achieves stable voltage regulation, balanced power sharing, and efficient energy curtailment. During fault conditions, the battery storage on both sides successfully supports the critical loads. The fuel cells are operated in power-controlled mode effectively up to their full rated 6$\mathrm{k}$$\mathrm{W}$ capacity while the DC bus voltage stabilization is ensured by the battery energy storage systems. These results validate the proposed EMS as a robust and low-complexity solution for maritime DC microgrids, offering stable voltage regulation, effective load prioritization, and resilient operation of critical loads. Full article

Off-grid electric vehicle (EV) charging infrastructure powered by hybrid solar–wind systems address critical adoption barriers in developing countries, where grid unreliability and sparse charging networks constrain transportation electrification. Despite growing research interest, no comprehensive review has systematically synthesized the interplay between hybrid renewable architectures, intelligent energy management strategies, and techno-economic viability specifically for off-grid EV charging in resource-constrained settings. This systematic review applies the PRISMA methodology to analyze 94 peer-reviewed publications (2013–2026), examining system architectures, intelligent control strategies, power electronics, battery storage, and deployment frameworks for standalone hybrid solar–wind EV charging stations. Key findings indicate that hybrid solar–wind configurations achieve 30–50% reductions in battery storage requirements and 15–25% lower levelized cost of energy (LCOE) (USD 0.08–0.15/kWh) compared with single-source systems, driven by diurnal and seasonal resource complementarity. Among intelligent control methods, the two-stage distributionally robust optimization (TSDRO) framework emerges as the most promising for data-scarce environments, outperforming conventional deterministic and stochastic approaches by 10–20% in managing renewable intermittency without requiring precise probability distributions. Wide-bandgap power semiconductors (SiC, GaN) enable 96–98% conversion efficiency, while lithium iron phosphate batteries provide 3000–5000 cycle lifetimes suited to tropical operating conditions. Critical gaps remain with field validation still predominantly simulation based, long-term operational data exceeding 24 months on equipment degradation and climate resilience are scarce, and scalable financing models for developing country contexts require further development. Nigeria is presented as an exemplar deployment context, with transferable insights for sub-Saharan Africa, South Asia, and Southeast Asia. Full article

Retrofit strategies to improve the energy performance of buildings have gained significant importance worldwide; however, asbestos in older residential buildings is considered a serious threat to both human health and the environment. Existing studies have generally focused on the health effects of asbestos, the properties of insulation materials, or individual aspects of energy performance, while a coherent and comparative conceptual framework for sustainable retrofit systems is limited. This review aims to systematically integrate the current scientific evidence on asbestos management, alternative insulation materials, life cycle assessment (LCA), and circular economy principles to present a literature-informed conceptual decision-support framework for sustainable retrofit. The study used the PRISMA-based literature selection approach, while the evidence from different peer-reviewed studies was comparatively organized in the context of process workflows, risk considerations, lifecycle impacts, and building-physics-related findings. The literature-based results indicate that incorporating safe asbestos management, low-carbon insulation materials, and circular retrofit strategies into an integrated approach can improve energy efficiency and environmental sustainability. However, this study is not based on a validated numerical simulation, an executed optimization model, or calibrated engineering analysis, but rather on a comparative synthesis and conceptual interpretation of the existing literature and presents a decision-support framework that can guide future low-carbon and safe construction strategies. Full article

Reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has become an effective architecture for supporting computation-intensive and latency-sensitive applications by enabling flexible deployment and enhanced wireless coverage. However, when non-orthogonal multiple access (NOMA) is incorporated, the joint optimization of computation offloading, wireless resource allocation, RIS phase configuration, and UAV trajectory design becomes highly challenging owing to the strong coupling among decision variables, problem non-convexity, and time-varying system dynamics. To address these challenges, this paper investigates the energy consumption minimization problem in a NOMA-based RIS-assisted UAV-MEC system by jointly optimizing user offloading ratios, transmit power, UAV computing resource allocation, and flight trajectory. A long short-term memory (LSTM)-embedded proximal policy optimization (PPO) algorithm is developed to capture the temporal dependencies of system states and enable adaptive decision-making in dynamic environments. In addition, a closed-form phase conjugation-based optimal RIS configuration is derived and incorporated into the environment model to reduce the action space and improve training efficiency. The simulation results show that the proposed LSTM-PPO method converges faster and achieves lower energy consumption than conventional PPO, deep deterministic policy gradient (DDPG), and fixed offloading schemes, while exhibiting improved stability and scalability in the tested multi-user scenarios. These results demonstrate the effectiveness of combining temporal learning and model-assisted RIS optimization for energy efficient resource management in RIS-assisted UAV-MEC systems. Full article

The present paper suggests a federated learning-based distributed solar forecasting model based on gated recurrent unit (GRU) networks (FL-GRU) to smart buildings in Muscat, Oman. The growing adoption of rooftop photovoltaic (PV) systems in urban settings needs precise, privatizing, and scalable forecasting models able to manage geographically dispersed and statistically heterogeneous data. The suggested solution will include federated learning and GRU networks to train a global forecasting model across several smart buildings and avoid the exchange of raw energy data to overcome these challenges. The local GRU models are trained on local PV generation data and only parameters of the model are relayed to a central aggregation server. This provides privacy of data without compromising the effectiveness of collaborative learning. The proposed framework is tested in a variety of realistic scenarios such as scalability analysis, non-identically distributed (non-IID) data, client dropout, communication constraints, seasonal variability, and privacy saving noise injection. Simulation outcomes show that the proposed FL-GRU model presents a final RMSE of 0.129, MAE of 0.100 and forecasting accuracy of 97%. When increasing the number of clients involved in the process, 2 to 10, RMSE decreases to 0.129, which supports the high scalability advantages. In non-IID scenarios, RMSE ranges between 0.129 and 0.167, and even with half of the clients dropping, the system is robust with an RMSE of 0.172. The proposed FL-GRU is better than the benchmark models, Local GRU, centralized GRU, FL-LSTM, and FL-ANN with a maximum improvement of 22.29% in RMSE reduction. Also, the best predictive consistency is found with correlation analysis with R² = 0.957. On the whole, the suggested approach can offer an efficient, privacy-aware, and scalable solution to distributed solar energy prediction in smart cities. Full article

The shift from fossil fuels to renewable energy is a key component in achieving global sustainability and dealing with climate change. Natural resources, such as sunlight, air, water, and biomass, have tremendous potential to create clean energy; however, exploiting these resources in an efficient, stable, and large-scale integration manner is difficult due to their variable and distributed nature. Artificial intelligence (AI) approaches that mimic human learning and decision-making have recently become viable approaches to solving renewable energy problems. This study mainly examines the current landscape of AI applications across solar, wind, hydro, geothermal, ocean, hydrogen, bioenergy, and hybrid energy systems. AI enhances renewable energy forecasting, improves power system frequency analysis and stability assessments, and optimizes dispatch and distribution networks. Beyond technical optimization, AI also contributes to broader sustainability goals, including energy efficiency improvements, intelligent smart grid management, and enabling mechanisms such as carbon trading and circular economy practices to reduce exposure to climate extremes. Drawing on evidence from a range of renewable energy areas, this review highlights the importance of AI in bridging the link between technological innovation and sustainable energy management. This paper discusses potential future research avenues, such as building sophisticated AI designs, energy-efficient chips, and data communication networks. Ultimately, the synergy between AI and renewable energy systems represents not only a technological advancement but also a transformative pathway toward a resilient, low-carbon future. Full article

Urban separate sewer systems face significant challenges from rainfall-derived infiltration and inflow (RDII) during the wet season. To achieve the integrated optimization of operational safety, energy consumption, and carbon emissions, this study proposes a dynamic optimal control method. A real-time regulation framework was developed by coupling a Storm Water Management Model (SWMM) hydraulic model with a Non-dominated Sorting Genetic Algorithm II (NSGA-II) multi-objective optimization algorithm within a Model Predictive Control (MPC) structure. Based on real-time water level risks, the framework adaptively adjusts the priority among three objectives: overflow reduction, pumping station energy consumption, and methane emission potential. Using a real separate sewer network in CZ city as a case study, the method was evaluated under light, moderate, and heavy rainfall scenarios. Results show that, compared with traditional rule-based control (RBC) and fixed-weight static model predictive control (SMPC), the proposed dynamic model predictive control (DMPC) strategy reduces overflow by 37.2% during heavy rain, and achieves 16.5% energy savings and a 15.8% reduction in methane emission potential during light rain. The strategy also balances network storage utilization, mitigates local overload, and demonstrates enhanced robustness to rainfall forecast errors, providing an effective technical solution for safe, energy-efficient, and low-carbon urban drainage operation. Full article

In this study, we investigate the impact of electric energy storage (EES) on phase line power flow symmetry and photovoltaic (PV) energy utilization in prosumer three-phase four-wire integrated household systems. The analysis is based on high-time-resolution (1 s) experimental data collected from a real household grid and subsequent simulations of energy flows using MATLAB/Simulink software. Two converter operation strategies were evaluated: the conventional symmetric mode and the asymmetric mode developed by the authors based on an adaptive power flow management algorithm. For both strategies, the impact of EES capacity on imbalance in the distribution system operator (DSO) grid was investigated. The methodology analyzes energy flows in each phase line separately, allowing for a detailed assessment of the imbalance between phase line phenomena and their impact on local energy consumption. Key performance parameters used for the efficiency evaluation include the self-consumption and self-sufficiency rates, which quantify the share of locally generated energy consumed within the household and the degree of independence from the DSO grid. The results show that combining adaptive asymmetric inverter control with appropriately sized energy storage allows for more efficient on-site utilization of PV energy, which, at the same time, improves the load symmetry of the phase lines in the DSO grid. Full article

To address the conflict between real-time performance and global optimality in the energy management of dual-motor plug-in hybrid electric vehicles (PHEVs), this paper proposes a model predictive control (MPC) strategy based on dynamic programming (DP). Firstly, a radial basis function (RBF) neural network is employed to predict future driving conditions, providing preview information for the MPC. Subsequently, a DP-MPC cooperative architecture is constructed, which invokes DP to solve for local optimal solutions during the receding horizon optimization process and incorporates linear reference SOC trajectory planning to approximate the global optimum. Simulation results under the WLTC driving cycle demonstrate that the fuel consumption of the proposed strategy is 2.311 L/100 km, representing a 33.2% reduction in pure fuel consumption compared to the rule-based (RB) strategy, and a 16.3% reduction in equivalent fuel consumption (including electricity converted to fuel based on the engine’s generation efficiency), while achieving 96.31% of the fuel economy of the global optimal DP strategy. The study validates that this method significantly improves fuel economy while guaranteeing real-time performance. Full article

In response to the dual pressures of energy consumption and environmental pollution faced by the global shipping industry, this paper proposes an optimization method for the energy storage capacity of a ship’s hybrid energy system based on a two-layer optimization model, aiming to enhance the energy utilization efficiency and operational stability of the system. A DNN-IPSO optimization framework integrating deep neural networks (DNN) and the improved particle swarm optimization algorithm (IPSO) was constructed, and combined with robust control strategies, it optimized the energy storage capacity configuration problem under complex dynamic conditions. The results show that the proposed method exhibits superior performance in terms of energy utilization efficiency, system dynamic response, and stability. The energy utilization efficiency of the system has been increased to 91.3%, the bus voltage fluctuation has been reduced to 3.98%, the load tracking error has been decreased to 17.6 kW, and the average convergence iteration times have been reduced to 71 times. The 17.6 kW load tracking error accounts for only 1.76% of the rated propulsion power of the 1 MW-level experimental platform, which is approximately 38% lower than that of the GA-PSO method. The experimental results on the real ship show that after using the DNN-IPSO optimization, the unit voyage energy consumption has been reduced to 41.7 kWh/km, the propulsion power stability coefficient has been increased to 0.956, the system transient recovery time has been shortened to 3.2 s, and the power reserve margin has been increased to 18.4%. The proposed method can effectively enhance the energy management capability, dynamic response performance, and operational stability of the ship’s hybrid energy system in the actual operating environment, providing reliable technical support for the engineering application of the integrated energy system of ships. Full article

The use of electricity to power vehicles is currently seen as a key opportunity for climate protection and the development of global economies. In this context, the aim of this article is to identify and consider the advantages of electric vehicles and the barriers to their development, as well as to present opportunities for leveraging knowledge from modern management to mitigate them. The study utilized desk research and qualitative methods. The results indicate that, despite significant consumer interest in electromobility in the European Union, the observed growth rate has been declining recently. This is due to a number of unfavorable administrative, technical, financial, and organizational conditions, which the researchers observed and present in this article. It turns out that eliminating these barriers is impossible without leveraging management knowledge, particularly in the areas of energy management and sustainable development. The article identifies specific solutions in the area of sustainable management, when implemented in practice, can contribute to increasing the efficiency of electricity use in transport, improving energy security, and protecting the natural environment. Full article

The circular economy offers a key pathway to achieve the joint improvement of resource conservation and carbon reduction, yet its causal effect on urban energy efficiency remains insufficiently examined. This paper takes China’s Zero-Waste City (ZWC) policy as a quasi-natural experiment and uses panel data from prefecture-level cities between 2006 and 2023. By applying staggered difference-in-differences and double machine learning methods, we evaluate the effect of urban circular economy transformation on energy efficiency. The results reveal four main findings: (1) The ZWC policy significantly improves energy efficiency in pilot cities. (2) The policy operates through three mechanisms: resource circulation, structural optimization, and innovation compensation. (3) Policy effects are stronger in environmentally regulated cities, large cities, and regions with higher artificial intelligence development. (4) The policy also generates broader benefits beyond energy savings, including coordinated fiscal, economic, and environmental gains. Overall, this paper highlights the spillover benefits of the circular economy from waste reduction to energy conservation and provides policy implications for coordinating waste management and energy transition at the urban level. Full article

This systematic literature review (SLR) synthesizes current research on integrating machine learning (ML) into robust optimization (RO) frameworks for solving Green Vehicle Routing Problems (Green-VRP) under uncertainty. The key contributions include utilizing the EmbedSLR 2.0 framework for objective screening, establishing a functional ML role taxonomy, and mapping uncertainty sets to computational tractability. Following PRISMA guidelines, searches across Scopus, Sage, and Dimensions identified 82 eligible studies validated through a three-point quality assessment scale. Bibliometric analysis indicates that the VRP has evolved into an interdisciplinary field that combines the power of rigorous RO with the integration capabilities of ML to achieve sustainability and resilience goals. Based on the results of the literature review, it was found that ML plays four crucial functional roles: as an end-to-end problem solver, a tool for predicting input parameters, a guide for search subroutines, and a mechanism for constructing more precise uncertainty sets. Various frameworks such as Adjustable Robust Optimization (ARO), Distributionally Robust Optimization (DRO), and Data-Driven Robust Optimization (DDRO) have been reported in various studies to offer improved cost efficiency and robustness compared to conventional static RO models by utilizing data more dynamically to reduce the level of conservatism. The integration of these environmental factors is carried out through emission and energy consumption parameters, which systematically give rise to operational trade-offs. This SLR has several limitations, including database and language limitations, the absence of cross-reference validation in EmbedSLR 2.0, and limitations in quality assessment. This publication is funded by the Universitas Padjadjaran through the LPDP on behalf of the Indonesian Ministry of Higher Education, Science and Technology and managed under the EQUITY Program (Contract No. 4303/B3/DT.03.08/2025 and 3927/UN6.RKT/HK.07.00/2025), as well as the Universitas Padjadjaran Research Grant under Research Grant for Graduate Students (Hibah Riset Melibatkan Mahasiswa Pascasarjana - RMMP) with contract number 5598/UN6.3.1/PT.00/2025. This systematic review was registered on the Open Science Framework (OSF) on 8 May 2026. Full article