World Electric Vehicle Journal

Accurate and timely identification of slippery road surfaces is essential for ensuring driving safety and operational efficiency on highways. However, blurred vehicle-background boundaries, uneven illumination, and water splashing caused by passing vehicles make existing image-based recognition methods prone to low accuracy. To address these challenges, this paper proposes an improved SegFormer-based framework with two task-specific innovations: (1) a novel Guided Multi-scale Fusion (GMF) module for task-guided multi-scale feature integration, designed to incorporate auxiliary information such as vehicle type, relative speed, and splash regions, enabling the network to focus on slipperiness-relevant road areas while suppressing background interference; and (2) an improved Boundary Attention Awareness (BAA) module with directional Sobel-based boundary initialization, which provides explicit geometric priors to preserve fine boundary details and reduce ambiguity in slippery regions with irregular or weak edges. A multi-scale input and enhancement strategy is further adopted, along with a weighted combination of cross-entropy loss and Dice loss to mitigate class imbalance. Experimental results on our self-constructed Guangzhou Beierhuan Expressway dataset achieve an mIoU of 95.80%, accuracy of 97.84%, and F1-score of 97.86%. To verify cross-domain generalization, we further evaluate the model on two additional benchmarks: it achieves an mIoU of 93.51% on the synthetic SYN-UDTIRI dataset, and attains an mIoU of 95.80% with an AmIoU of 76.20% on the public Cityscapes dataset, achieving competitive performance against several mainstream architectures. The proposed method offers considerable application potential for highway safety warning systems. Full article

The rapid electrification of marine transportation has increased the number of hybrid marine microgrids with the addition of renewables and energy storage. The continuously varying propulsion loads, fluctuating sea states, and renewable intermittency introduce significant challenges in bidirectional power transfer and power quality enhancement in marine vessels. This work presents a power-oriented operational strategy for a hybrid Roll-on/Roll-off (Ro-Ro) ferry-based marine microgrid (FMG) integrating diesel generators (DGs), Solar Photovoltaic (PV) arrays, and battery energy storage systems as the primary power sources. The proposed FMG adopts a hybrid AC/DC bus configuration linked through a bidirectional voltage source interlinking converter (ILC). The ILC facilitates multiple functionalities, including effective load compensation, mitigation of Total Harmonic Distortion (THD), continuous power support through bidirectional energy exchange, maintenance of balanced sinusoidal currents, and unity power factor (UPF) operation, thereby providing an integrated solution for improved power quality and reliable microgrid performance. A supervisory control (SC) is devised to operate the FMG seamlessly under islanded modes depending on the availability of power sources. To achieve the above-mentioned objectives, a power-optimized Dual Power-based Instantaneous Power Theory (DP_IPT) is employed and it involves a Sequential Delay Signal Cancelation (SDSC)-based Phase-Locked Loop (PLL) for the effective extraction of sequence components, so as to address the unbalance and nonlinearities in an effective manner with reduced oscillations. The proposed control strategy reduces diesel generator utilization through the effective integration of Solar PV and battery support during anchoring operation. The integration of renewable energy sources substantially enhances clean energy utilization, resulting in the reduction of overall carbon emissions, accounting for a near-40% decrease in emissions compared with the conventional diesel generator (DG)-based operating mode. The proposed FMG and control framework are validated through the Hardware-in-the-Loop (HiL) approach employing an OPAL-RT (OP4512) real-time controller. The HiL investigations demonstrate the efficacious working of the proposed control in achieving less carbonized and enhanced power quality operation for next-generation electrified hybrid maritime microgrids. Full article

The article addresses the problem of estimating the specific electric energy consumption of urban electric transport under real operating conditions. It is substantiated that standardized driving cycles and rated energy consumption values do not always accurately reflect the actual operating modes of vehicles on urban routes, since energy consumption is affected by speed conditions, road conditions, passenger load, ambient temperature, auxiliary systems operation, and the number of stops, accelerations, and braking events. A simplified engineering model is proposed for adjusting the baseline specific electric energy consumption by means of a system of correction factors, which makes it possible to adapt the calculation to specific operating conditions under limited availability of telematics data. A distinctive feature of the proposed approach is the possibility of using a baseline energy consumption value determined from a driving cycle or vehicle specification data, followed by its adjustment according to the characteristics of an actual route. The proposed methodology was experimentally verified using certified trolleybus test data representing a vehicle with characteristics similar to a 12 m urban battery electric bus; however, further validation using dedicated battery electric bus datasets is required. For the reference vehicle operating on route No. 15 in Lutsk, with a route length of 10.2 km, the calculated electric energy consumption was 17.853 kWh at full mass and 12.498 kWh at curb mass, corresponding to approximately 1.75 and 1.23 kWh/km, respectively. The results were compared with experimental data and recent literature sources. The proposed methodology is intended for preliminary engineering assessment of electric energy consumption when detailed operational data are unavailable. Full article

Greenhouse gas emissions from heavy-duty vehicles (HDVs) must be drastically reduced. Battery electric trucks (BETs) are the main option for low-carbon road freight transport, but they require recharging infrastructure. However, a thorough cost analysis of public charging is lacking, especially for the Megawatt Charging System (MCS). This study estimates the infrastructure-related levelised cost of megawatt charging for battery electric trucks in Europe based on simulated truck operations and techno-economic modelling. The analysis combines empirical driving data with cost assumptions for MCS infrastructure. The reported values are infrastructure-only costs and include annualised capital expenditure, installation costs, grid connection costs and operating expenditure. They exclude electricity prices, taxes, levies, land costs and operator margins. Low- and high-cost scenarios differ in assumed charger hardware and installation costs, while grid connection costs and utilisation assumptions are held constant across scenarios. The results show that utilisation is the key driver of cost reductions over time. The infrastructure-related levelised cost of MCS declines to 0.03–0.07 EUR/kWh by 2050 under the analysed cost assumptions. The total annual infrastructure costs for Europe are estimated at 6.6–10.8 billion EUR, or 2.9–4.7 EUR cents/km. The results support policy decisions on infrastructure deployment and highlight the importance of coordinated rollout and demand growth. Full article

While the adoption of electric vehicles can reduce CO₂ emissions, the extent of this reduction depends on the growth of the EV market. Sigmoid growth models, such as logistic or Gompertz function models, can be used to predict expected EV sales trends; however, their quality has not yet been comprehensively analyzed, as this would require looking into the future to compare today’s predictions with future data. Since this is obviously not possible, this study takes a retrograde approach. It uses the available historical data to create forecasts that are then compared with the actual values from subsequent years. For example, a forecast based on data from 2010 to 2014 can be compared with the values achieved in years from 2015 to 2025. The quality of the functions is assessed using fit indices. Among the ten distinct functions tested, including two equivalent Gompertz functions, and under the stated saturation assumptions, the Gompertz family offers the most stable retrospective forecasts of EV stock (prediction period MAPE of 16.0% for the global data, against 15.1% for the generalized logistic, which performs comparably). The generalized logistic attains marginally better global point accuracy, whereas Gompertz is preferred for its greater stability across forecast origins and its more interpretable parameters. Full article

The transition toward electric vehicle (EV) architectures exceeding 800 V offers key advantages, including shorter charging times, lower operating currents, and reduced system weight due to smaller conductor cross-sections, all of which enhance overall vehicle performance. However, identifying suitable traction inverter topologies that meet automotive requirements for efficiency, electromagnetic interference (EMI), and reliability remains critical. This study presents a simulation-based converter-level electro-thermal and conducted-EMI benchmark of 2-Level H-Bridge, 3-Level Active Neutral-Point Clamped (ANPC), and 3-Level T-Type inverters under identical output-power operating conditions. The distinguishing feature of this work is the unified evaluation of these topologies under a common external thermal boundary, enabling a consistent comparison of semiconductor losses, junction-temperature behaviour, cooling-burden indicators, conducted-EMI tendencies, and first-order lifetime-oriented thermal indicators. Within this framework, the required effective thermal resistance is used as a cooling-burden indicator, while junction-temperature swing and mean junction temperature are used as relative thermal-stress indicators. Under the considered simplified R–L loading conditions, the results show that multilevel topologies reduce semiconductor losses, peak junction temperature, conducted-EMI excitation, and relative thermal-stress indicators compared with the 2L H-Bridge. These findings are interpreted as comparative topology-level trends under the defined converter-level simulation framework rather than as final vehicle-level EMI compliance or power-module lifetime predictions. Full article

The rapid growth of electric vehicle (EV) adoption presents significant challenges for power system stability while creating new opportunities for decentralized energy management. Peer-to-peer (P2P) energy networks have emerged as a promising approach for transforming EVs from passive loads into coordinated grid assets. This paper presents a comprehensive review of EV-P2P integration through a three-layer architectural framework that systematically connects physical infrastructure, market mechanisms, and intelligent control strategies. The Physical Layer reviews how V2X technologies and bidirectional charging enable EVs to operate as flexible storage resources and ancillary service providers. The Transactional Layer reviews on blockchain-based platforms, auction mechanisms, and game-theoretic models for secure energy trading. The Intelligence Layer reviews advanced control strategies, including decentralized optimization methods such as the Alternating Direction Method of Multipliers (ADMM) and Deep Reinforcement Learning. Collectively, the reviewed studies demonstrate that these approaches enable EVs to operate as flexible loads, distributed storage resources, and ancillary service providers, while improving energy trading efficiency, reducing operating costs, and alleviating network congestion under simulated operating conditions. Despite these promising results, a substantial gap remains between simulation-based studies and practical implementation. Future research should prioritize integrated pilot projects to evaluate scalability, interoperability, cybersecurity, and regulatory compliance under realistic operating conditions. Full article

Electric vehicle manufacturing firms face increasing supply chain vulnerability due to component shortages, technological interdependence, raw material volatility, and demand uncertainty. This study aims to examine whether digital transformation can be translated into a resilience-building capability and to identify the transmission channels. Using panel data from Chinese A-share listed electric vehicle manufacturing firms from 2014 to 2023, we employ a double machine learning framework to estimate the relationship between digital transformation and supply chain resilience while accounting for high-dimensional firm-level controls. The results show that digital transformation is positively associated with supply chain resilience. This finding remains robust across alternative sample-splitting ratios, different machine learning algorithms, alternative winsorization thresholds, and sample restrictions. It also holds after addressing potential endogeneity using instrumental variable estimation. Mechanism tests indicate that digital transformation contributes to resilience by promoting technological innovation and reducing managerial transaction costs. Heterogeneity analysis further shows that the effect is more pronounced among firms with stronger market positions, larger firms, and vehicle manufacturers. These findings suggest that digital transformation is not merely a tool for operational upgrading but also an important organizational capability for strengthening supply chain resilience in electric vehicle manufacturing. Full article

Driven by the dual-carbon target strategy, the new energy vehicle industry has achieved large-scale and rapid development. As the core protective component of power batteries, the surface quality of battery shells directly determines the operational safety and reliability of batteries. However, defects such as scratches, pits, and cracks easily occur on battery shells during forming processes, including stamping and deep drawing. Traditional manual detection suffers from bottlenecks, such as high labor intensity, low detection efficiency, and high false detection rates, making it difficult to adapt to the large-scale and high-cycle production requirements of modern industry. Firstly, this study systematically elaborates the material system, preparation process, and defect formation mechanism of battery shells, and clarifies the coupling mechanisms of material properties, process parameters, and equipment and environmental conditions for defect evolution. Subsequently, it compares and analyzes the principles, advantages and disadvantages, and applicable scenarios of traditional machine-vision- and deep-learning-based detection technologies, and focuses on analyzing the application performance and optimization paths of single-stage and two-stage object detection algorithms in shell defect recognition. Furthermore, it addresses the core challenges of deep-learning-based battery shell defect detection technologies in data, algorithm deployment, detection dimensions, and other aspects, and proposes targeted optimization strategies. Finally, the development directions, such as system integration and online learning, are forecasted. This study can provide theoretical support and technical references for the intelligent manufacturing of battery shell stamping and forming, as well as for surface defect detection. Full article

The rapid adoption of electric vehicles (EVs) has intensified the demand for advanced energy storage technologies capable of delivering high energy density, high power density, enhanced safety, and extended service life. Although lithium-ion batteries remain the dominant energy storage technology for EVs, their limited power capability, thermal degradation, and accelerated aging under high transient loads constrain vehicle performance. Battery–supercapacitor hybrid energy storage systems (HESSs) have emerged as a promising solution by combining the high energy density of batteries with the high-power density and rapid charge–discharge capability of supercapacitors. However, the increasing complexity of HESS architecture requires intelligent energy management strategies to optimize power allocation, battery protection, thermal regulation, and overall system efficiency. Existing review papers primarily address individual aspects of HESS architecture, battery management, or artificial intelligence (AI)-based control, leaving a lack of a unified review integrating these topics. This paper addresses this gap by reviewing 181 publications published between 2020 and 2026, covering HESS architectures, conventional and AI-driven energy management strategies, machine learning, deep learning, reinforcement learning, battery state estimation, diagnostics, prognostics, thermal management, and fault diagnosis. The reviewed studies are critically analyzed to assess the impact of AI on battery lifetime, regenerative braking, charging performance, thermal behavior, and energy efficiency. The review further identifies emerging research directions, including explainable AI, digital twins, federated learning, edge intelligence, vehicle-to-grid integration, and cybersecurity-aware energy management. The findings indicate that AI-based approaches generally demonstrate greater adaptability, predictive capability, and battery protection than conventional methods under dynamic operating conditions, although challenges related to computational complexity, real-time implementation, data availability, explainability, cybersecurity, and standardization remain significant barriers to large-scale deployment. Full article

Electric vehicles equipped with in-wheel motors provide packaging, controllability, and drivetrain-simplification advantages; however, the increase in wheel-side unsprung mass can intensify vibration transmission to the chassis, seat, and occupant. This paper presents a coordinated active seat and active suspension control strategy for an integrated 8-DOF quarter-car model that includes an in-wheel motor, an active seat suspension, and a 4-DOF seated driver body model. The proposed controller combines a Harmony Search (HS)-optimized proportional–integral–derivative (PID) feedback baseline with a repeatable-disturbance feedforward compensation term. The HS-PID loop provides baseline transient attenuation, while the feedforward term compensates the repeatable component of the bump-induced disturbance transmitted through the coupled seat–vehicle system. The controller is evaluated against passive suspension, active-seat-only control, active-vehicle-suspension-only control, and an HS-PID baseline under repeated bump/shock excitation. The results show that coordinated actuation reduces occupant displacement and acceleration responses relative to the benchmark cases. The discussion explains the active-seat-only peak-acceleration amplification, the different magnitudes of displacement and acceleration improvements, and the practical implications of suspension stroke and actuator-force limits. The reported conclusions are therefore confined to the repeated bump/shock condition considered in this numerical study; broader ride-comfort generalization requires standardized whole-body vibration metrics, random-road validation, speed variation, parametric uncertainty analysis, and drivetrain energy evaluation. Full article

This paper presents a comprehensive review of control techniques, simulation mechanisms, and validation methods applied to DC-DC converters, with a focus on high-step-up topologies used in renewable energy systems such as photovoltaic and wind power applications. Control strategies including classical PID, fuzzy logic, sliding mode, and model predictive control (MPC) are analyzed in terms of performance, robustness, and implementation complexity. Simulation platforms and hardware-in-the-loop (HIL) validation frameworks are also discussed as key enablers for rapid prototyping. The findings reveal a clear trend toward intelligent and hybrid control schemes that combine nonlinear techniques with artificial intelligence to address the inherent nonlinearities and parametric uncertainties of DC-DC converters. However, challenges remain in real-time implementation due to computational demands, which drives the need for future developments focused on the (i) integration of AI-based controllers with low-cost embedded platforms, (ii) standardization of HIL-based validation workflows, and (iii) optimization of converter topologies for specific applications such as electric vehicle charging and photovoltaic grid integration. Looking forward, the convergence of advanced control algorithms, real-time validation platforms, and application-specific converter design is expected to define the next generation of power electronics systems, enabling more efficient, reliable, and scalable renewable energy integration. Full article

This study presents an empirical fleet-level assessment of 110 autonomous-range trolleybuses using anonymized records collected over 12 months. The dataset comprises 40,150 vehicle-day operating records, 40,150 energy records, 3960 pack-month SOH records, and 584 maintenance, failure, and downtime events. Outcomes are reported in absolute units: RUB/km for LCC, kg CO₂-eq/km for ELC, events per 100,000 km, and downtime hours per 10,000 km. Autonomous operation accounted for 24.5% of mileage. Average net energy consumption was 1.520 kWh/km, whereas mode-distributed gross energy was 1.521 kWh/km in contact-supply mode and 1.752 kWh/km in autonomous mode. The daily-energy model achieved a full-sample fit of R² = 0.860 and MAPE = 8.119%. Validation of vehicle-grouped data using the generated dataset showed R² = 0.842 and MAPE = 8.74%. Mean SOH decreased from 89.98% to 85.94%, accompanied by higher internal resistance. In the central 6.5-year scenario, diagnostic-gated strategy B2 reduced estimated LCC from 29.52 to 26.16 RUB/km. The event-weighted control effect by RPN decreased from 125.4 to 80.4 (35.9%). Baseline ELC decreased only from 0.6646 to 0.6594 kg CO₂-eq/km because operational electricity dominated the total. The contribution is an observation-linked framework that integrates vehicle-day operation, pack-month diagnostics, and event-level maintenance data to compare cost, emissions, and risk under explicit battery-eligibility and service-coverage constraints. The novelty is therefore the empirical, observation-level coupling and joint calibration of existing energy, battery-condition, life-cycle, and reliability methods within one auditable fleet workflow, rather than the introduction of a new standalone degradation or reliability model. Full article

Autonomous vehicles (AVs) are moving from pilots to regular urban service, yet the speed of large-scale implementation remains uncertain. While prior research emphasizes technological feasibility and adoption, less attention has been paid to the socio-technical dynamics that constrain deployment. This study examines how unintended consequences shape the pace of AV implementation in cities. Drawing on a mixed-methods design combining a structured scoping review with 18 expert interviews, interrelated dynamics are identified across institutional, behavioral, economic-platform, spatial, and normative-societal domains. The findings indicate that implementation speed is not determined by technology alone but emerges from reinforcing feedback loops that generate systemic frictions, including governance lag, demand rebound, spatial bottlenecks, and legitimacy challenges. The study advances a systems-oriented framework that conceptualizes implementation speed as an emergent property of socio-technical dynamics, highlighting the importance of adaptive and anticipatory governance for sustainable urban mobility transitions. Full article

Vehicle Ad Hoc Networks (VANETs) face critical challenges in trust management, privacy preservation, and scalability, particularly with the integration of 5G networks in Intelligent Transportation Systems (ITS). Traditional centralized trust models present single points of failure and privacy concerns that compromise network security and user anonymity. This paper presents a novel decentralized trust model leveraging blockchain technology, Interplanetary File System (IPFS) integration, and post-quantum cryptographic algorithms to address these limitations. Our proposed TrustChain-VANET framework implements advanced privacy-preserving encryption techniques including threshold and homomorphic encryption, geographical sharding for scalability, and edge-assisted consensus mechanisms. Performance evaluation demonstrates significant improvements: 40% reduction in authentication latency (90–120 ms vs. 150–300 ms), 90% malicious node detection rate (+15% improvement), 300% increase in transaction throughput (2000–2150 TPS), and 100% scalability enhancement supporting up to 5000 nodes. The system integrates seamlessly with 5G network slicing (URLLC, eMBB, mMTC) while maintaining quantum resistance through CRYSTALS-Dilithium, KYBER, and FALCON algorithms. Real-world deployment considerations including OBU computational constraints, standardization gaps, and energy efficiency are comprehensively analyzed. Results indicate that the proposed decentralized approach provides robust security, enhanced privacy, and improved scalability for next-generation vehicular networks, making it suitable for large-scale ITS deployment. The main contribution of this work is the development of a unified TrustChain-VA 48NET framework. The proposed framework integrates blockchain-based trust management, IPFS-assisted storage, 5G network slicing, Mobile Edge Computing (MEC), geographical sharding, and post-quantum cryptographic mechanisms within a single architecture for next-generation VANET environments. While these technologies have been investigated separately in previous studies, this work presents a consolidated framework that analyzes their interoperability, identifies integration challenges, and evaluates their combined impact on trust management, scalability, privacy preservation, and deployment feasibility in Intelligent Transportation Systems. Full article

Electric-vehicle gearboxes operate at high rotational speeds and under low acoustic masking, making tonal excitation and unit-to-unit variability important design concerns. Contemporary loaded tooth contact, multibody, finite-element, and vibroacoustic models can represent the nominal excitation–transfer–response–radiation chain in considerable detail, but their inputs often remain disconnected from the manufactured and assembled gearbox. This review develops a structured framework for identifying which physical parameters, numerical representations, and validation evidence are required before a model can credibly represent a nominal design, a tolerance space, an as-built unit, or a production population. Parameters are classified jointly based on the physical origin and noise, vibration, and harshness (NVH) function and are mapped to contact, system-dynamic, structural, acoustic, and hybrid data-driven models. Four simulation-readiness levels are defined: nominal, tolerance-based, measurement-based, and variability-aware. Explicit transition gates, validation quantities, and permitted claims are assigned to each level. A stage-specific validation matrix distinguishes contact-level, interface-force, structural-response, and acoustic evidence. Literature-grounded quantitative examples demonstrate validated elastic multibody modeling and manufacturing-data-based gear-whine prediction while clarifying the limits of the available evidence. The framework provides a traceable basis for model planning, measurement selection, uncertainty analysis, and readiness-aware reporting of electric-vehicle gearbox NVH simulations. Full article

With the increasing complexity of electrical and electronic (E/E) components in modern powertrains, functional safety requires more systematic assessment methods. This paper presents a simulation-based approach for automated Hazard Analysis and Risk Assessment (HARA) of battery electric vehicles in accordance with ISO 26262. The method combines operational-situation parameters, including vehicle speed, road surface, vehicle gap, and road inclination, to define a structured set of hazardous events. Severity, Exposure, and Controllability are evaluated using rule-based criteria, including a dedicated Controllability rule set for longitudinal motion hazards. Quantitative erroneous acceleration and deceleration thresholds associated with different ASILs are derived using a bisection search algorithm, enabling quantifiable and testable safety goals. Comparison with manual HARA reveals systematic biases: low speed does not necessarily imply improved Controllability due to shorter vehicle gaps, while Severity may be underestimated at low speeds because of high instantaneous electric-machine torque. The maximum ASIL is often identified similarly by simulation and experts, whereas lower-ASIL hazardous events may lack consistency and coverage in manual HARA. As a practical application, the approach can be integrated into existing automotive safety workflows as a HARA support tool, improving the consistency of lower-ASIL events while allowing engineers to focus on maximum ASIL cases. Full article

Connected and Automated Electric Vehicles (CAEVs) are rapidly evolving into complex Cyber-Physical-Social Systems (CPSS), generating structural tensions between technological innovation and public safety. Current research in public governance exhibits significant fragmentation. Scholars frequently isolate data privacy compliance from algorithmic safety auditing, treating them as distinct silos. To bridge this gap, this study applies the PRISMA framework to systematically synthesize 135 core peer-reviewed articles, exposing the endogenous limitations of unidimensional regulatory paradigms. Our analysis yields three central insights. First, traditional “notice-and-consent” models fail under the ubiquitous data collection demands of modern V2X environments. Macro-level policies must translate into foundational Privacy-Enhancing Technologies (PETs) through “Law-as-Code” mechanisms. Second, the opacity of end-to-end algorithmic decision-making deconstructs traditional tort liability systems. This necessitates ex-ante quantitative auditing mechanisms—such as Explainable Artificial Intelligence (XAI) and enhanced Threat Analysis and Risk Assessment (TARA 2.0)—to mitigate adversarial attacks and physical-level safety hazards. Third, overcoming cross-national regulatory fragmentation requires constructing a “dual-track synergistic” governance architecture. This framework institutionalizes the coupling of data lifecycle quality workflows with the algorithmic Safety of the Intended Functionality (SOTIF). Ultimately, this review advocates for adaptive regulatory sandboxes and advances the harmonization and mutual recognition of global standards (e.g., ISO/SAE 21434, UN R155/156). Addressing current methodological and empirical data constraints, future academic inquiry must pivot. Researchers should target the value alignment challenges of Large Language Models (LLMs) in autonomous driving and implement multi-stakeholder participatory policy pilots designed to reconcile diverse social values. Full article

With the growing demand for electricity, the penetration of Renewable Distributed Generators (RDGs), alongside the transition from Internal Combustion Engine Vehicles (ICEVs) to Electric Vehicles (EVs), has become a pressing challenge for the stable and efficient operation of distribution networks. This research focuses on a critical task of determining the optimal integration of RDGs, including solar photovoltaic systems, wind turbines, biomass units, and EV charging stations, into an Unbalanced Radial Distribution System (URDS). This work proposes an optimization approach aiming to minimise the total costs (TCs), active power losses (APLs), voltage unbalance factor (VUF), and voltage deviation (VD) of the network under consideration simultaneously. The integration of RDGs is carried out using a metaheuristic technique, which accounts for the intermittent nature of renewable energy sources, the stochastic behaviour of EVs, and the variability of load demands over 24 h a day. Fuzzy decision-making is applied to select an optimal trade-off solution from the Pareto front. The effectiveness of the developed approach is assessed comprehensively on a Pakistani 60-bus URDS as a primary study, while the IEEE-123 bus system is employed as a validation case to demonstrate the applicability and scalability of the proposed methodology. Among the five analysed case studies, the simulation results indicate that coordinated integration of RDGs and EVCSs into the system yields significant benefits, including a decreased reliance on conventional centralised generation, with a reduction of 56.29% in costs, 46.61% in losses, 7.17% in voltage unbalance, and 27.13% in voltage deviation as compared to the base case. Full article