Search Results (5,541)

Fault detection from electric vehicle charging segments is challenging because real-world records are noisy, verified fault labels are scarce, and weak signatures may evolve gradually across time and unevenly across a vehicle’s charging history. This study proposes an unsupervised dynamic multiscale state-space support vector data description framework for vehicle-level battery fault detection. A gated diagonal state-space encoder preserves long-range charging patterns while retaining a direct input path for transient changes. A progressive cross-scale fusion module then combines short-term fluctuations with accumulated deviations in the learned hidden representation. Finally, a two-stage hypersphere optimisation strategy first estimates the normal centre and then refines the boundary around that fixed centre. This coordinated design avoids sequence reconstruction and directly scores charging segments by their distance from normal behaviour before robust vehicle-level aggregation. On the two EVBattery subsets that permit statistically reliable evaluation, the proposed framework achieved vehicle-level areas under the receiver operating characteristic curves of 0.8849 and 0.8438. These results exceed those of the best-performing baseline on the corresponding subsets by 0.0889 and 0.0417, respectively. The results show that coordinating long-range encoding, cross-scale fusion, and staged boundary learning improves threshold-independent vehicle-level fault ranking in real charging data. Full article

The rapid adoption of electric vehicles, together with increased battery mass and altered load distribution, is placing greater demands on ride comfort and suspension adaptability, while controller optimization may still request forces beyond the instantaneous capability of the physical semi-active actuator if experimentally supported force limits are not explicitly enforced. This study proposes a unified experimentally constrained optimization framework for a magnetorheological damper (MRD)-based semi-active suspension system using a two-degree-of-freedom quarter-car model. The damper is characterized at eleven current levels and represented by a branch-dependent lookup model that provides the zero-current baseline and instantaneous feasible force range. Proportional–integral–derivative (PID) and linear quadratic regulator (LQR) controllers are independently tuned using a genetic algorithm (GA) and particle swarm optimization (PSO) under identical vehicle dynamics, objective functions, tuning excitation, and MRD force constraints. Each candidate force demand is projected onto the experimentally derived feasible range throughout optimization. The controllers are tuned on a composite B–C–D profile and subsequently evaluated over nine road–speed scenarios. PID-PSO reduces the RMS sprung-mass acceleration by 15.91% and achieves the best acceleration performance in six cases, whereas LQR-PSO provides more balanced improvements in body motion, suspension travel, tire response, and force feasibility. The proposed framework therefore provides a more physically constrained basis for the comparative design and evaluation of MRD-based semi-active suspension control. Full article

At highway merging sections, merging vehicles must enter the mainline traffic flow within a limited acceleration section. Under high traffic demand, this process often involves rapid acceleration or deceleration. Conventional merging assistance control has mainly focused on reducing acceleration and ensuring safe inter-vehicle gaps. However, when electric vehicles (EVs) are considered, battery energy consumption is also an important evaluation perspective. This study evaluates the effects of different speed adjustment strategies for merging vehicles on EV battery energy consumption and smooth merging performance at a single highway merging section. Four cases are compared: Acceleration-Minimizing Merging Control (AMC), which minimizes the absolute value of the required acceleration; Fixed-Arrival-Time Energy-Minimizing Merging Control (FEMC), which minimizes battery energy consumption under a fixed arrival time; Variable-Arrival-Time Energy-Minimizing Merging Control (VEMC), which minimizes battery energy consumption without fixing the arrival time; and a no-control case. EV battery energy consumption is calculated by integrating battery-side power over time, considering driving resistance, inertial force, drivetrain efficiency, regenerative braking efficiency, maximum regenerative power, and auxiliary power. The simulation results show that AMC is advantageous in terms of smooth merging performance, whereas VEMC achieves the lowest overall average battery energy consumption. FEMC and VEMC reduced battery energy consumption by up to 10.7% and 39.5%, respectively, compared with AMC under the evaluated initial-speed conditions, although their smooth merging performance decreased under some conditions; however, their smooth merging performance remains lower than that of AMC, and the energy-saving effect depends on the initial speed and traffic demand conditions. These results indicate that EV-oriented merging assistance control requires a control design that considers the trade-off between energy efficiency and smooth merging performance. Full article

Connected, automated, shared, and electric (CASE) vehicle concepts are considered a promising approach for improving the sustainability of urban mobility by increasing vehicle utilization and reducing fleet size. While the energy consumption of conventional battery electric vehicles has been investigated extensively, the influence of fleet size, speed limits, and ambient temperature on the energy demand of CASE vehicle fleets has received little attention. This study presents a simulative consumption analysis of an all-electric CASE vehicle fleet based on the EDAG CityBot concept. A validated microscopic traffic simulation of the city center of Darmstadt, Germany, is coupled with a backward-facing powertrain model and detailed secondary consumer models to determine the total fleet energy consumption under varying operating conditions. The analysis considers fleet sizes between 20% and 100% of a reference fleet, together with a 17% fleet size scenario, which allows for the fulfillment of the urban mobility demand according to the vehicle system provider. Besides fleet size, three urban speed limit scenarios and five ambient temperature scenarios are evaluated. Among the investigated fleet size scenarios, the lowest mean fleet energy demand is observed at a fleet size of 20%, resulting from the opposing effects of increasing driving energy consumption and decreasing secondary consumer energy consumption. However, the difference between the 20% and 17% scenarios is not statistically significant. Furthermore, the study demonstrates that secondary consumers, particularly automated driving hardware and heating, ventilation and air conditioning systems, represent a major contribution to the total energy consumption of CASE vehicles and must therefore be considered in fleet-level energy analyses. Although an individual CASE vehicle exhibits higher average energy consumption than a conventional battery-electric vehicle, primarily due to its greater average weight and rolling resistance, an increase in utilization of more than 16% would be sufficient to offset this disadvantage. Full article

The building sector is one of the largest contributors to global energy demand and carbon emissions, making the transition to net-zero buildings (NZBs) a critical component of climate change mitigation strategies. However, the persistent building energy performance gap (BEPG), defined as the discrepancy between predicted and actual energy consumption, continues to hinder the achievement of net-zero operational performance. Accordingly, this review examines the role of artificial intelligence (AI) in enabling NZBs through the integration of energy-efficient building systems, renewable energy technologies, and intelligent operational control. A comprehensive review of the literature published between 2018 and 2025 was conducted, focusing on three complementary domains: heating, ventilation, and air conditioning (HVAC) system efficiency as the demand-side pillar, renewable energy integration as the supply-side pillar, and AI as the enabling layer connecting both domains. Synthesis of the reviewed literature reveals that demand-side HVAC technologies achieve energy savings ranging from 20% to 67%, while supply-side renewable energy integration increases photovoltaic (PV) self-consumption by 11–13%. Furthermore, AI-driven optimization, particularly through reinforcement learning (22.3% ± 8.4% energy savings) and digital twins (up to 70% renewable energy utilization), substantially enhances building performance within integrated energy management frameworks. The reviewed studies further demonstrate that AI techniques, including machine learning, deep learning, reinforcement learning, and digital twins, enable accurate energy forecasting (R² > 0.90), intelligent operational control, and effective coordination of integrated PV–battery energy storage system–electric vehicle systems, improving building energy flexibility and reducing grid fluctuations by up to 12.78%. Despite these advances, challenges related to data quality, interoperability, model explainability, cybersecurity, and limited large-scale real-world validation remain significant barriers to widespread adoption. Overall, the evidence indicates that AI serves as a key enabler for reducing the BEPG and improving the reliability, resilience, and operational efficiency of NZBs, thereby supporting the transition toward intelligent, low-carbon built environments. Full article

Electric vehicle (EV) fires can rapidly affect smoke and thermal conditions in underground parking garages. In this study, fifteen PyroSim/FDS cases were screened, but quantitative analysis was restricted to four prescribed-extraction cases and one baseline-matched two-device spray pair. The 0.30 m production mesh was selected using characteristic-fire-diameter, geometric-resolution, and computational-cost criteria. A matched 0.20/0.30/0.50 m check yielded non-monotonic fixed-point responses; mesh independence was not established. Extraction cases were compared using 270–300 s means and the first downward crossing of a 10 m visibility reference. At the same nominal outflow of 10 m³/s, two 5 m/s surfaces produced lower M1 gas temperature and CO and higher visibility than one 10 m/s surface. Relocating the second spray device beneath the vehicle reduced the τ = 120–150 s mean M4 underside-region gas temperature from 776.5 to 103.4 °C, while M1 visibility remained about 0.22 m. Because the model lacks a physical make-up-air path and corresponding experiments were not reproduced, these findings are limited to local prescribed-boundary comparisons. Relevant experiments support the represented mechanisms but the results do not validate the absolute point values. The simulations do not demonstrate battery extinguishment, maintained tenability, or code compliance. Full article

Battery electric vehicle (BEV) fires in road tunnels can intensify thermal, smoke transport, visibility, and CO exposure hazards under confined ventilation. This study evaluated the combined mitigation performance of water mist and semi-transverse mechanical ventilation. A three-dimensional PyroSim/FDS model of a 200 m × 10 m × 5 m tunnel was established with a 7 MW BEV design fire at the midpoint. The prescribed-source model was assessed against a reduced-scale lithium-ion battery tunnel experiment; at the representative monitoring location, the simulated temperature history reproduced the main trend, with deviations of approximately 7% and 10% at the first and second peaks. Thirty-six coupled cases examined ventilation mode, nominal opening velocity, nozzle arrangement and spacing, flow rate input, droplet diameter, and spray cone angle. Supply ventilation improved hot-smoke-layer cooling and visibility, whereas exhaust ventilation more effectively reduced the local CO volume fraction. Under the baseline weighting scheme, the highest-ranked case reduced the peak local ceiling-region and near-fire gas temperatures by 77.8% and 82.2%, increased average visibility during 200–500 s by 42.9%, and achieved a comprehensive relative mitigation index (CRMI) of 56.6%. Two supplementary nominal 10 MW simulations showed that this case retained substantial thermal control, reducing the two peak temperatures by 65.7% and 74.1%, but did not improve local visibility or CO. Thus, the thermal-mitigation trend persisted at the higher nominal input, whereas the full multi-hazard ranking was not transferable across fire sizes. Full article

Maintaining frequency stability in islanded microgrids (MGs) has become increasingly challenging due to the growing penetration of renewable energy sources, particularly photovoltaic systems, wind turbine generators (WTGs), and plug-in hybrid electric vehicles (PHEVs). The intermittent nature of renewable generation and continuous load variations introduces significant power imbalances, resulting in frequency deviations and degraded system stability. Although the classical Ziegler–Nichols (ZN) tuning method is attractive because of its simplicity and ease of implementation, it is generally limited to conventional proportional–integral–derivative (PID) controllers and is often inadequate for renewable-dominated MGs. To overcome these limitations, this paper proposes a modified ZN-based tuning strategy for a novel multistage PID (MPID) controller. Unlike the conventional ZN method, the proposed approach extends its applicability to the MPID structure by introducing an additional proportional gain (KPP), enabling the tuning of five controller parameters while preserving low computational complexity and practical implementation. The proposed controller is implemented and validated using a detailed MATLAB/Simulink model of an isolated MG comprising PV systems, WTG, diesel generators, and PHEVs. Its performance is comprehensively evaluated under multi-step load disturbances, renewable power fluctuations, combined disturbances, and different PHEV charging/discharging modes and battery state-of-charge levels. Furthermore, the proposed controller is benchmarked against conventional ZN-PID, ZN-FOPID, and both PID- and MPID-based controllers tuned using Particle Swarm Optimization, Cuckoo Search Algorithm, Moth–Flame Optimization, and Grasshopper Optimization Algorithm. Simulation results demonstrate that the proposed ZN-MPID controller achieves the best overall dynamic performance, with a settling time of 4.109 s, zero overshoot, a maximum frequency undershoot of 1.801 × 10⁻⁴ Hz, and the lowest error indices (ISE = 3.073 × 10⁻⁶, ITSE = 0.697 × 10⁻⁶, and ITAE = 3.40 × 10⁻⁴). Compared with the investigated metaheuristic-based PID controllers, the proposed controller reduces the settling time by up to 86.1% and the error indices by up to 95.5%. It also consistently outperforms all investigated MPID tuning methods, confirming the effectiveness of the proposed modified ZN tuning strategy. Overall, the proposed methodology provides an efficient, low-complexity, and practical solution for frequency regulation in renewable-dominated isolated MGs. Full article

The transport sector contributes nearly 20% of New Zealand’s total greenhouse gas emissions, making it crucial for interventions to meet the 2050 net-zero target. Transitioning to electric vehicles (EVs) presents a sustainable solution but poses challenges in electricity distribution due to unpredictable EV charging behaviours. This research addresses these challenges by developing three mathematical models that optimise EV charging patterns, manage power flow along distribution lines and incorporate battery storage systems. Using the Tāmaki area as a case study, the models analyse total energy demand and optimal battery storage size, revealing that a 3.49 MWh battery system could mitigate the projected 2040 peak daily grid energy demand of 541.5 MWh and avoid costly power line upgrades. The study also introduces a vehicle-to-grid (V2G) integration model, showcasing its potential to reduce grid dependence and improve energy utilisation. The findings provide critical insights for Auckland’s electricity distribution companies, supporting strategic asset upgrades and offering evidence-based guidance for government policies on EV adoption. In summary, this research provides innovative solutions for optimising EV charging infrastructure, benefiting utility companies and policymakers by informing data-driven decisions. The comprehensive approach, which includes power flow, battery storage, and V2G technology, presents a scalable framework for international cities facing similar challenges, promoting global sustainable transport solutions towards achieving international climate targets and sustainable urban development. 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

Fuel cells (FCs) are critical for decarbonizing the transport industry, with Life Cycle Assessment (LCA) serving as the standard evaluation framework. However, existing literature exhibits severe methodological heterogeneities and divergent system boundaries that introduce deep epistemic uncertainties. This review conducts a systematic analysis to critically harmonize FC environmental performance across the road, aviation, and maritime sectors. Quantitative synthesis reveals global warming potential (GWP) as the dominant metric. For light-duty vehicles, GWP drops to around 30 gCO₂eq/km, matching battery-electric configurations exclusively under deeply decarbonized grids. Manufacturing FC stacks and advanced storage imposes a severe upfront carbon debt, particularly prominent in heavy-duty freight (60–130 tCO₂eq). In aviation, 80–90% in-flight GWP reductions trigger massive burden-shifting, transferring 60–70% of lifecycle damages to ground-based infrastructure. Maritime FCs shrink GWP to 0.06–0.60 kgCO₂eq/kWh, strictly contingent on upstream hydrogen production. Crucially, despite long-term GWP advantages, FC pathways face systematic penalties in acidification, eutrophication, and ecotoxicity, heavily driven by platinum-group catalysts and fluoropolymer membranes. By isolating software-driven biases and database discrepancies, this work delivers an actionable methodological roadmap, establishing a policy-aligned baseline for future FC transportation sustainability frameworks. Full article

Battery thermal management systems (BTMSs) are essential for maintaining the performance, efficiency, durability, and safety of electric-vehicle battery packs. Although fin-enhanced air-cooled BTMSs offer a simple and leakage-free cooling solution, their practical implementation is often limited by increased weight, insufficient temperature uniformity, and restricted heat-dissipation capability under high thermal loads. This study numerically investigates a compact air-cooled BTMS for two types of cylindrical lithium-ion batteries using aluminum and polypropylene (PP-$\beta$) circumferential fins in inline and staggered cell arrangements. Unlike previous fin-based air-cooling investigations, the present study combines a compact 2 × 4 battery pack with transverse and longitudinal center-to-center cell pitches of 1.2D, a direct comparison between metallic and lightweight polymer fins, and an assessment of two 18650 battery types with different capacities, thermophysical properties, and heat-generation characteristics. A three-dimensional steady-state conjugate heat-transfer model was developed in ANSYS Fluent to evaluate the effects of fin number, fin material, cell arrangement, ambient temperature, and inlet airflow velocity under discharge rates ranging from 1 C to 4 C. The results reveal that increasing the number of fins consistently reduced the maximum cell temperature but increased the pressure drop. The inline configuration generally achieved a lower maximum temperature and higher Nusselt number (Nu), whereas the staggered arrangement maintained a substantially lower pressure drop. Relative to the corresponding finless configurations, the Nu increased by 64.4–71.2% for the inline arrangement and 86.4–98.1% for the staggered arrangement. Polypropylene fins provided thermal performance close to that of aluminum fins in terms of maximum temperature while reducing the total fin mass by approximately 44.8%; however, aluminum fins maintained better temperature uniformity. These findings quantify the trade-offs among thermal performance, pressure drop, compact cell spacing, and system weight, providing design guidance for compact fin-enhanced air-cooled BTMSs. Full article

Electric vehicles are emerging as a sustainable alternative to conventional transportation. However, route planning in Internet of Vehicles environments remains challenging because conventional routing algorithms based on travel distance or time do not adequately consider electric vehicle-specific constraints. Existing routing strategies often overlook the combined effects of battery energy, traffic congestion, and charging requirements, resulting in inefficient routing decisions. This paper proposes an adaptive Energy, Congestion, and Charging-Aware Routing (ECCAR) protocol that uses energy-feasibility verification, cost-based charging-station selection, and route re-optimization for electric vehicles in Internet of Vehicles environments. ECCAR integrates residual battery energy, traffic congestion, travel time, charging station availability, and charging delay into a unified routing decision framework. It first evaluates whether the remaining battery energy is sufficient to reach the destination. Otherwise, it identifies all reachable charging stations and selects the one that minimizes the routing cost rather than the nearest station. After charging, the route is recalculated using traffic and charging information obtained through V2V and V2I communications. Simulation results demonstrate that ECCAR reduces total energy consumption by up to 19.4%, travel time by up to 25.3%, and charging waiting time by up to 39.1% compared with existing routing schemes. These results demonstrate the benefits of integrating energy, traffic, and charging information for reliable electric vehicle routing in dynamic Internet of Vehicles environments. Full article

The rapid growth of battery energy storage systems, renewable energy integration, electric vehicles, and DC microgrids has significantly increased the demand for compact, efficient, and bidirectional isolated DC–DC converters. Conventional Dual Active Bridge (DAB) converters commonly employ high-frequency transformers to provide galvanic isolation and bidirectional power transfer. Although transformer-based DAB converters offer excellent performance, their magnetic components increase converter volume, weight, core losses, leakage inductance, manufacturing complexity, and overall cost. Consequently, recent research has explored alternative high-frequency energy transfer techniques based on capacitive coupling, aiming to reduce magnetic components while preserving efficient resonant power conversion.This paper proposes a Capacitively Coupled Dual Active Bridge (CC-DAB) converter employing high-power metallized polypropylene (MKPH) capacitors as the high-frequency energy transfer medium. The proposed converter operates at a relatively low switching frequency while investigating the safe operating conditions of the capacitive coupling network to ensure reliable and efficient power transfer. A microcontroller-based single-phase-shift (SPS) modulation strategy is implemented to generate the gate-driving signals of the full bridges, whereas the switching frequency is selected to achieve zero-voltage switching (ZVS) throughout the investigated operating range. The phase-shift angle ($\varphi$) regulates the transferred power by controlling the voltage difference between the primary and secondary bridges across the capacitive coupling network. The proposed converter is analyzed theoretically and validated through simulation and experimental testing. Experimental results demonstrate stable bidirectional power transfer, soft-switching operation, and a peak conversion efficiency of 91.3% at a relatively low switching frequency of 52 kHz. The experimental verification confirms the practical feasibility of capacitive coupling for resonant bidirectional power conversion and demonstrates its potential as an alternative architecture for low- and medium-power applications requiring compact size, high efficiency, reduced magnetic component requirements, and reversible energy transfer. Furthermore, the proposed topology contributes to the ongoing development of transformerless resonant converters by experimentally validating a high-frequency capacitive coupling network capable of supporting efficient bidirectional power flow under practical operating conditions. Full article

Hardware sizing and energy management for hybrid energy storage systems are usually designed sequentially, hiding the interactions between them. This paper proposes an MPC-informed dynamic screening framework in which every candidate configuration is simulated under one model predictive control law over a complete driving cycle, so that operational behaviour, not static metrics, determines selection. A fully documented post-evaluation criterion aggregates tracking, battery electrical stress, soft constraint violations and design overhead into one score normalised against an exact baseline anchor. Because one evaluation costs about 60 ms, the complete exact Pareto front of an electric transit bus case study is screened, not a sample. The static design cost proves almost uninformative regarding dynamic performance: the rank correlation between the two orderings is statistically indistinguishable from zero, the sets that they rank highest share no member, and the statically cheapest design falls far down the dynamic ranking, ending below the baseline. The cause is structural opposition on the pack voltage, which improves the dynamic performance but raises the static cost. The framework returns a leading design family that improves on the baseline overall, quantifies the battery stress that its leaner supercapacitor incurs, and shows the verdict to be robust to controller tuning but dependent on the duty and control strategy. Full article