Search Results (584)

This study investigates the side-impact crashworthiness of a low-floor electric bus with traction batteries integrated into the inter-window pillars of the body structure. A finite-element model of the bus body was developed in Ansys and used to evaluate six impact scenarios involving conventional diesel and battery-integrated configurations. The analysis included evaluation of von Mises stresses, structural safety margins, deformation fields, strain energy, and transient nodal velocity response. The battery-integrated configuration demonstrated improvements in key crashworthiness indicators across the investigated impact scenarios, with both the average maximum deformation and the averaged equivalent stress reduced by approximately one quarter compared with the conventional configuration. The stress state of the inter-window pillars remained below the local structural failure levels observed in the conventional configuration, with the maximum pillar stress criterion reduced by more than half. Simultaneously, lower transient nodal velocities indicated reduced transmission of impact momentum toward the occupant compartment and more efficient redistribution of impact energy through the body structure. The results demonstrate the feasibility of using battery-integrated inter-window pillars as multifunctional structural members that simultaneously serve as energy storage and enhance the side-impact crashworthiness of low-floor electric buses. Full article

The baseline assessment analysis for total cost of ownership of the pilot demonstrations of the ESCALATE project was carried out for four different powertrain configurations, dealing with modular and scalable powertrains for various vehicle configurations in long-haul trucking. The baseline TCO methodology and results for battery electric trucks (BETs), fuel cell electric trucks (FCETs) and FC range-extending BETs are analysed based on the final designs of the demonstrator vehicles and their foreseen pilot use cases and operational scenarios. As real operation data is not yet available, the analysis relies on energy use and pilot mission analysis through simulation. Overall, the TCO analysis shows several key factors affecting the relative competitiveness of the different zero-emission powertrains and vehicles. Long-haul operations pose clear challenges to vehicle design and long-range vehicles on single charge or refill show increased curb weight, limiting allowable payload due to GVW limits. The best payload capacity is shown for opportunity charging BETs and FCETs. BETs are generally the closest competitor to conventional trucks, but a key factor is the relative energy price difference between diesel, electricity (private or public) and hydrogen. Energy sourcing will be an important factor for end users to enable competitive shift to zero-emission options. Access to cheap private electricity or local green hydrogen may facilitate a choice between the options. Full article

Accurate short-term forecasting of residential electricity demand is increasingly important for smart distribution systems, particularly in the context of demand-side management and flexibility-oriented grid operation. In this study, a high-resolution forecasting framework is proposed in which household electricity demand is classified into fixed, shiftable, and adjustable load categories and forecasted together with total load. A one-minute-resolution synthetic residential load dataset is generated using the Centre for Renewable Energy Systems Technology (CREST) demand model for households with two to five occupants over a 31-day winter period in January. The appliance-level demand data are grouped according to operational characteristics and integrated into a representative four-bus distribution feeder. Minute-level power flow analysis is then performed to calculate technical losses, which are incorporated into the forecasting dataset together with meteorological variables (temperature, wind speed, and solar irradiance) and temporal descriptors. Using this multi-input structure, random forest (RF), support vector machine (SVM), feed-forward neural network (FFNN), and long short-term memory (LSTM) models are comparatively evaluated for the prediction of fixed, shiftable, adjustable, and total residential loads. Model performance is assessed using root mean square error (RMSE) and Pearson correlation coefficient (R), while mean absolute error (MAE) is additionally reported for the final test set. The results show that the LSTM model provided the most consistent overall forecasting performance, particularly for shiftable, adjustable, and total load estimation, while RF yielded competitive results for fixed-load correlation and short-window forecasting in Buses 1 and 2. In contrast, SVM and FFNN exhibited weaker generalization performance across several load categories. The proposed framework provides a practical foundation for the development of dynamic pricing mechanisms that consider load-type-based controllability levels. Overall, the findings demonstrate that integrating load categorization with meteorological, temporal, and technical loss information provides a robust and reproducible framework for smart grid applications such as demand-side management, peak load mitigation, and flexibility-aware residential load analysis. Full article

Real-time monitoring of lithium-ion capacitors (LICs) is crucial for ensuring reliability and predictive maintenance in dynamic applications such as electric transportation. However, traditional electrochemical impedance spectroscopy (EIS) techniques are complex and costly for onboard diagnostics due to their reliance on external excitation signals and dedicated hardware. Therefore, this paper presents an innovative framework for online state of health (SOH) estimation that bypasses these limitations by utilizing fast Fourier transform (FFT)-based passive impedance extraction directly from operational current and voltage signals. From experimental data, the equivalent circuit model (ECM) is developed, as well as its parameters, such as ohmic resistance, charge-transfer resistance, and Warburg diffusion. These parameters are identified through the extraction of impedance points in the low frequency region through FFT and the series resistance point using ohmic measurement, then performing a periodic curve fitting to these points. These curve fittings provide extracted ECM parameters. These parameters are used with a trained model to estimate the SOH of the monitored cell and are updated online. The proposed method was experimentally validated on five LIC cells aged under various C-rates (1C, 4C, 7C) and temperatures (35 °C, 40 °C, 50 °C), showing consistent impedance evolution with capacity fade. Validation of the utilized machine learning models, such as Polynomial Regression (PR), principal components analysis (PCA), and random forest (RF) regression, achieved SOH prediction errors as low as 2.23% compared to experimental results. The developed framework is particularly suitable for applications such as flash-charged electric buses but is broadly applicable across other energy storage systems as well. This advanced method enables real-time diagnostics without hardware modification, offering significant potential for integration into existing battery management systems (BMSs). Full article

Modern vehicles depend on over-the-air (OTA) updates to maintain security, safety, and functionality, but the attack surface of an update campaign depends on the underlying electrical/electronic architecture and on the controls assumed for update orchestration. This article reports a bounded, model-based TARA study for distributed, domain-based, and zonal vehicle architectures, which is implemented in Medini Analyze and interpreted against ISO/SAE 21434, UNECE Regulations R155 and R156, and ISO 24089 guidance. The comparison unit is not an architecture’s absolute security level. It is the architecture-conditioned instantiation of OTA trust anchors, update paths, STRIDE threat classes, evidence obligations, control-dependency assumptions, and sensitivity of model outputs under a harmonized TARA workflow. The model indicates that distributed architectures expose heterogeneous endpoints and legacy buses, and domain architectures reduce endpoint sprawl while elevating telematics and domain controllers as trust anchors. Moreover, zonal architectures can consolidate orchestration and monitoring under hardening assumptions while concentrating assurance obligations around high-performance computers, backbones, and Zone Controllers. Sensitivity checks show that raw threat counts, High/Critical counts, and severity distributions are model-granularity- and assumption-sensitive; they are therefore reported as diagnostics for traceability and evidence planning, not as real-world security rankings. The contribution is a reproducible interpretation of where OTA threat instances, trust boundaries, and regulatory evidence burdens move as vehicle E/E architectures change. Full article

This study presents an Ant Colony Optimization (ACO)-based methodology for the optimal placement of lithium-ion battery energy storage systems (BESSs) in radial electrical distribution networks. The proposed framework integrates base-case power-flow assessment, critical-bus identification, discrete BESS siting, technical–economic objective evaluation, and post-optimization validation. The methodology is applied to the IEEE 33-bus radial distribution test system, where the initial operating condition is characterized in terms of nodal voltage profile, voltage deviation, voltage-stability index, active-power losses, and annual loss cost. The optimization process identifies buses 13 and 31 as the most suitable locations for two identical BESS units, with the reported validation case evaluating each unit at upper admissible capacity limits of $1000\mathrm{kW}$ and $4000\mathrm{kWh}$. The obtained results show that the optimized BESS allocation increases the minimum voltage profile to values above $0.94\mathrm{p}.\mathrm{u}.$, raises the voltage-stability index to more than 0.88, reduces active-power losses to approximately $0.0166\mathrm{p}.\mathrm{u}.$, and decreases the annual cost associated with active-power losses by more than $66\%$ relative to the base case. Additional validation through sensitivity analysis, repeated stochastic runs, operating-mode evaluation, and comparison against a genetic algorithm confirms the consistency and robustness of the proposed ACO-based methodology. The results demonstrate that the proposed framework provides a technically consistent and computationally accessible solution for improving voltage regulation, reducing feeder losses, and lowering loss-related operating costs in radial distribution systems. Full article

The optimal reconfiguration of electrical distribution feeders is a fundamental strategy for reducing active power losses and improving voltage profiles, yet it remains a challenging mixed-integer nonlinear programming (MINLP) problem due to the combinatorial explosion of radial topologies and the nonlinearities introduced by power flow equations. This paper proposes a novel master–slave methodology that integrates a Chu and Beasley genetic algorithm (CBGA) with a minimum spanning tree (MST)-based repair mechanism to address these challenges. In the master stage, the CBGA explores the binary space of switching decisions via steady-state population management, duplicate elimination, and stagnation restart policies. A key contribution lies in the MST-based repair procedure, which ensures that every individual generated by crossover and mutation is projected onto a feasible radial and connected configuration, effectively confining the search to the constrained solution space without recourse to penalty functions. A systematic weight-design rule preserves the Hamming distance between infeasible offspring and repaired solutions, minimizing the distortion of genetic information. The slave stage evaluates each candidate topology using a successive approximations power flow solver, assessing electrical feasibility and computing active power losses. The proposed methodology is validated on multiple test feeders, ranging from small 9- and 24-bus networks to large-scale benchmarks including 33-, 69-, 84-, 136-, and 415-bus systems. A comparison against the deterministic sequential switch opening method (SSOM) and a specialized tabu search demonstrates that the CBGA-MST consistently matches the best-known optima in the literature, achieving loss reductions of up to 9.63% compared to SSOM on the 415-bus system. A statistical analysis over 100 independent runs confirms the algorithm’s robustness, with zero standard deviation for networks of up to 69 buses and a standard deviation of only 2.99 kW (0.51%) for the 415-bus system. The findings confirm that the proposed approach offers superior scalability, robustness, and solution quality, positioning it as a practical and effective tool for distribution system operators seeking to enhance network efficiency under peak load conditions. Full article

Weak-bus identification is a key task for online security assessment, preventive control, maintenance verification, and resilience-oriented dispatch of interconnected power grids. In large-scale grids, conventional full-graph graph neural networks preserve the complete network topology but may become inefficient when many operating scenarios must be screened repeatedly. Direct graph partitioning improves computational tractability, but it may cut tie-line channels and weaken the boundary evidence that determines cross-area risk propagation. To address this trade-off, this paper proposes a boundary-compensated partition-based parallel graph neural network for weak-bus identification. The method first constructs a scenario-aware weighted power-grid graph and divides it into electrically coherent subgraphs under coupling-strength and partition-size constraints. Local graph encoders are then executed in parallel to learn intra-partition vulnerability representations. A boundary compensation module further restores cross-partition information by weighting tie-line neighbors according to electrical coupling, branch loading, and cross-area association. Standardized partition scores are finally fused into a whole-grid weak-bus ranking, and a composite learning objective jointly considers node-score regression, boundary consistency, and pairwise ranking stability. The method is evaluated on the IEEE 57-bus benchmark with mechanism-based node and branch vulnerability labels. Compared with the original full-graph GNN, the proposed method reduces the mean square error from 0.0359 to 0.0147, improves the Spearman rank coefficient from 0.248 to 0.446, and increases Hit@10 from 30% to 70%. Topological interpretation further shows that the identified weak buses are concentrated around high-risk branches such as 8-12, 12-14, 0-14, and 7-8, indicating that the proposed framework captures local aggregation, boundary transmission, and corridor-driven vulnerability propagation. The IEEE 57-bus benchmark is used as a focused validation case because it provides aligned node- and branch-level vulnerability evidence for evaluating weak-bus ranking behavior. Because the available aligned vulnerability evidence is concentrated in this medium-scale benchmark, the results should be interpreted as a focused validation of the proposed ranking mechanism rather than as a complete large-system scalability study. Full article

The reduction in driving range during winter remains a major barrier to the widespread adoption of battery electric buses (BEBs), as battery performance degradation and increased Heating, Ventilation and Air Conditioning (HVAC) energy demand significantly raise total energy consumption. This study investigates the use of proton exchange membrane fuel cells (PEMFCs) as auxiliary power units for range-extended electric buses (FC-REEBs) under low-temperature conditions to address this challenge. A comprehensive dynamic model was developed in MATLAB/Simulink 2025a version, integrating a fuel cell system, lithium-ion battery, power conversion unit, vehicle dynamics, and cabin thermal model. The model was evaluated under the World Harmonized Vehicle Cycle (WHVC) to compare three fuel cell operation strategies defined by fuel cell capacity and operating modes for cabin heating and battery charging. Performance was compared in terms of SOC variation, fuel cell loading patterns, hydrogen consumption, and equivalent fuel economy. Results indicate that the high-capacity strategy improves SOC stability but increases hydrogen consumption and reduces overall efficiency. In contrast, the strategy prioritizing cabin heating with minimal battery charging effectively utilizes waste heat and achieves the highest equivalent fuel economy. These findings highlight key trade-offs among different operating strategies and demonstrate that fuel cells can significantly enhance BEB efficiency and driving performance in cold environments while reducing battery load. Full article

Background: Smart-city road and intersection management increasingly aims to smooth bus operations and reduce stop-and-go driving, but cities often lack auditable indicators linking routine fleet data with comparable energy and environmental KPIs. Methods: This study develops a Monitoring–Reporting–Verification (MRV) workflow for daily bus records from a 2024 Polish metropolitan fleet (diesel, compressed natural gas (CNG), hybrid, and battery-electric buses). Records were quality checked, harmonized to MJ/km, aggregated to bus-month observations, and analyzed using a linear mixed-effects model with propulsion technology, season, and activity level as fixed effects and vehicle-level random intercepts. Environmental impacts were then calculated under well-to-wheel (WTW) boundaries using Environmental Footprint 3.1 (EF 3.1) impact categories, Poland’s 2024 electricity mix, and illustrative electricity-mix scenarios through 2050. Results: Relative to diesel, BEV and HEV were associated with lower adjusted energy intensity (ratios 0.272 and 0.681, respectively), whereas the CNG–diesel contrast was directionally higher but statistically inconclusive under the available CNG sample. BEV energy intensity more than doubled in winter in descriptive terms, and vehicle-specific heterogeneity remained high (ICC ≈ 0.61). The BEV climate profile improved under electricity decarbonization, while some EF categories showed mix-dependent trade-offs. The 3–10% traffic-management variants are interpreted as screening assumptions rather than measured ITS effects. Conclusions: Routine bus records can support auditable MRV and preliminary screening of fleet and corridor interventions, but causal traffic-management evaluation requires route-level trajectory, congestion, and before–after data. Full article

This study examines the impact of increasing photovoltaic (PV) penetration on the transient stability of the IEEE 9-bus power system. Synchronous machines are modeled with standard subtransient dynamics, while PV units are represented as current-limited grid-following inverters. Transient stability is assessed through the Critical Clearing Time (CCT) and the post-fault dynamic behavior, obtained from time-domain simulations carried out in MATLAB/Simulink^® R2023b. Two permanent three-phase faults are considered: a primary contingency on line 7–5 and a secondary contingency on line 9–6, introduced to assess the robustness of the observed trends across different fault locations. The results show an increase in CCT as PV generation progressively replaces the active power supplied by synchronous machines, whose inertia is therefore maintained: from 210 ms (0% PV) to 440 ms (25%)/1080 ms (40%) at bus 5, 410 ms (25%)/1130 ms (40%) and 290 ms (25%)/650 ms (40%) at buses 6 and 8, respectively, demonstrating that the penetration site is a key factor for system stability. For distributed penetration among the three buses, CCT values of 340 ms (25%) and 1020 ms (40%) highlight the significant influence of PV placement at bus 8. The fault on line 9–6 consistently yields higher CCT values across all scenarios, confirming the robustness of these trends independently of fault location. Although an overall increase in CCT was observed, higher PV penetration also led to more pronounced oscillations and operability issues after the fault. In particular, 75% of the penetration scenarios under the fault on line 9–6 do not meet the active power recovery requirements of IEEE 1547-2018 and IEEE 2800-2022, a result more severe than that observed for the fault on line 7–5. These results underscore that a higher CCT does not guarantee operational compliance, and that stability-oriented control strategies—such as grid-forming operation, fast active power support, and dynamic voltage control—remain essential. They also suggest that planning practices should favor interconnections electrically closer to the slack generator. Overall, a high PV penetration level—modifying only the operating point of synchronous machines—allows longer fault durations to be tolerated; however, appropriate siting of PV units and the adoption of advanced inverter controls could mitigate the observed oscillations and post-fault operability challenges. Full article

The electrification of urban buses under forthcoming Euro 7 regulations requires new structural solutions ensuring both safety and efficient mass distribution. This study proposes a novel conceptual electric bus body structure with battery-integrated inter-window pillars, in which energy storage systems are embedded. The concept was evaluated using finite element analysis in ANSYS under UNECE R66 rollover conditions by comparing an original diesel configuration (O-model) with a battery-integrated electric (B-model) one. Despite a substantial increase in body mass (from 1947 to 5464 kg), the B-model demonstrated improved structural performance. The maximum deformation decreased from 1489.5 to 1319.7 mm, while the difference between the control point displacements decreased from 32.21 to 12.68 mm. The average relative deformation of pillars decreased from 8.48% to 3.59%, and the intrusion amplitude was reduced from approximately 566 to 167 mm. Analysis showed comparable peak von Mises stresses (414.62 MPa vs. 439.19 MPa), but the B-model exhibited a 6.7% reduction in critical regions and a 16.9% decrease in average stress levels. The B-model remained within the elastic regime at the end of the simulation, whereas the O-model showed residual plastic deformation. The results indicate that integrating battery systems into load-bearing pillars leads to improved structural stiffness and deformation behaviour under rollover conditions, while full certification-level verification of UNECE R66 compliance is beyond the scope of the present study. Full article

The increasing penetration of converter-interfaced renewable energy sources has led to reduced system inertia and increased frequency stability challenges in modern power systems. Battery energy storage systems (BESSs) provide fast active power support. However, their effectiveness depends on the installation location, power rating, and network characteristics. This paper proposes a power-flow-informed, sensitivity-based method for the optimal placement and sizing of distributed BESSs to improve the frequency nadir and rate of change of frequency (RoCoF). The method integrates marginal frequency sensitivity obtained from time-domain simulations with network coupling information derived from power-flow analysis within a constrained optimization framework solved using particle swarm optimization. The network coupling weight, derived from voltage sensitivity, represents the steady-state electrical connectivity and active power redistribution capability, rather than transient frequency dynamics. It is used in combination with frequency sensitivity to improve the discrimination of candidate buses. The method is evaluated on the IEEE 39-bus system under multiple generator outage contingencies. For the most severe contingency (G01), the baseline system exhibits a frequency nadir of 55.9230 Hz and an RoCoF of 0.2404 Hz/s. With the proposed method, the frequency nadir improves to 58.6561 Hz, corresponding to an increase of 2.7330 Hz (4.88%), while the RoCoF is reduced to 0.1224 Hz/s (49.17% reduction). The optimal solution distributes a total BESS capacity of 298 MW across multiple buses, with the largest allocation of 46 MW at Bus 36. Across additional contingencies, the proposed method consistently achieves higher frequency nadirs and lower RoCoFs compared with both the baseline system and benchmark placement methods. The results demonstrate that combining dynamic frequency sensitivity with power-flow-based network coupling provides a physically consistent and computationally efficient strategy for distributed BESS allocation in low-inertia power systems. Full article

This paper presents an initial estimate of the energy demand and charging power requirements for the initial replacement of internal combustion public transport buses by battery electric buses in Chillán and Chillán Viejo, Ñuble Region, Chile. The analysis is based on an operational baseline derived from route lengths, service frequencies, departures, and fleet data, combined with official energy and transport demand projections. A case study is conducted for the introduction of 10 battery electric buses on Line 13 of public transport, comparing 100% overnight depot charging and 80% overnight charging complemented by opportunity charging. Results show that the initial 10-bus deployment would require an installed depot charging power between 300 and 450 kW, depending on the charging strategy, with an annual delivered energy of 1149.75 MWh. Long-term scaling scenarios suggest that bus-dedicated charging infrastructure could require between 4.52 MW and 22.35 MW by 2050. Although the numerical results are specific to the case study, the main contribution of this study lies in an operationally grounded planning framework that links the sizing of pilot routes with long-term charging demand scenarios and charging infrastructure projections, providing a transferable basis for preliminary electric bus planning in other mid-size or emerging regions facing similar infrastructure constraints. Full article

This study assesses the impact of regenerative braking on lithium-ion battery aging and operational efficiency of lithium-ion batteries in urban electric buses using a Rainflow-based cycle-counting framework. A previously developed simulation platform based on Energetic Macroscopic Representation (EMR) is employed to reproduce realistic daily driving cycles. Battery degradation is quantified by combining the Rainflow Counting Method with Miner’s Rule, enabling cumulative damage assessment across different depth of discharge (DoD) levels and regenerative braking intensities, k_br. Four representative cycling profiles—fixed 50%, 60%, and 70% DoD and a variable mixed-use scenario—were simulated under regenerative braking intensities ranging from 0% to 100%. Results indicate that regenerative braking extends average battery lifespan by approximately 0.9 years while increasing daily driving range by around 6 km. Profiles with lower DoD values, particularly when combined with moderate regenerative braking (k_br ≈ 0.3), achieved the most favourable balance between cycle induced degradation and energy recovery. Although higher DoD scenarios deliver greater mileage gains, they also accelerate capacity fade. The variable cycling profile demonstrated robust and consistent performance, highlighting the benefits of route and load variability. Additionally, lifetime mileage analysis demonstrates that intermediate DoD levels combined with regenerative braking maximize cumulative energy throughput while preserving service life. Overall, the proposed methodology offers a computationally efficient and practically applicable approach for battery life assessment under dynamic operating conditions, offering valuable insights for optimizing energy management strategies and electric bus fleet operations. Full article