Search Results (2,307)

Lithium-ion battery (LIB)-powered locomotives have emerged as a promising alternative to conventional rail electrification by reducing dependence on overhead catenary systems, lowering infrastructure costs, and improving operational flexibility. However, train failures involving power loss or mechanical faults present significant challenges for battery-powered rail systems, particularly on single-track corridors where alternate route options are limited. Consequently, the implementation of a reliable limp-home strategy is more complex than in road electric vehicles (EVs), requiring consideration of battery availability, rescue logistics, and track accessibility. This study investigates limp-home operation and rescue planning for a battery-powered historic trolley operating on a 20 km heritage route in North Carolina. The trolley is powered by a dedicated LIB trailer and supported by battery-charging (BC) infrastructure based on inductive power transfer (IPT) technology. A comprehensive framework is developed that integrates time–space analysis, cellular automata (CA)-based failure-risk modeling, and battery-energy assessment to evaluate train-failure scenarios and recovery strategies. The results identify critical failure regions along the route and demonstrate the benefits of strategically deploying additional LIB rescue trailers and wireless power transfer (WPT) infrastructure. A revised rescue strategy incorporating two additional LIB trailers, together with static and dynamic WPT systems, substantially reduces recovery time and improves the likelihood of maintaining scheduled excursions. Emergency energy analysis further shows that WPT-assisted operation can significantly extend limp-home capability under low state-of-charge conditions. Based on the integrated analysis, a tiered limp-home decision framework is developed to support operator decision-making during train failures. The proposed methodology provides a practical approach for enhancing the resilience, recoverability, and operational reliability of battery-powered heritage rail systems and can serve as a foundation for future battery-electric rail applications. Full article

Hydrogen-powered heavy-duty vehicles (HHDVs) operating under multiple driving conditions are subjected to coupled longitudinal, vertical, and lateral dynamic excitations, which significantly affect their dynamic performance and rollover stability. To investigate these characteristics, a coupled vehicle dynamic model consisting of a vertical dynamic model and a yaw–roll dynamic model was established, and numerical simulations were conducted under multiple operating conditions. The effects of operating condition, road roughness, initial braking speed, and braking deceleration on ride comfort and dynamic tire load were systematically analyzed. Furthermore, rollover stability was evaluated under J-turn, Fishhook, and Double Lane Change (DLC) maneuvers using yaw rate, slip angle, lateral acceleration, and lateral load transfer ratio (LTR) as evaluation indices. The simulation results show that braking causes the greatest deterioration in ride comfort, with the peak human–seat vertical acceleration increasing by 33.10% compared with the constant-speed condition, while acceleration results in a 27.55% increase. Road roughness substantially affects both ride comfort and dynamic tire load. Under braking, the peak front and rear tire dynamic loads on a Class D road are approximately 3.1 and 2.9 times those on a Class B road, respectively. Increasing the initial braking speed intensifies dynamic responses, whereas increasing the braking deceleration effectively suppresses tire dynamic load fluctuations. Among the three steering maneuvers, the Fishhook maneuver exhibits the highest rollover propensity, with the maximum absolute LTR approaching 0.8. These simulation-based findings provide insights into chassis parameter optimization, vehicle dynamic performance evaluation, and rollover prevention of HHDVs under multiple operating conditions. The present study is limited by the lack of experimental validation of the developed dynamic models, and experimental or hardware-in-the-loop validation will be considered in future work. Full article

Hispanics of Mexican American descent in South Texas show a very high prevalence of MASLD, with some studies reporting rates as high as 50% in adults. However, assessment of genetic risk factors underlying this prevalence is complicated by a high co-occurrence of other metabolic disorders and variable endogenous and exogenous environmental risk factors. To map the transcriptomic architecture of MASLD hepatic steatosis risk, we conducted an epidemiological-scale investigation using human induced pluripotent stem cell (iPSC)-derived hepatocyte cultures from 193 participants in our longitudinal South Texas Family Study (STFS). iPSC-based models offer greater power to map genetic risk factors by experimentally controlling for confounding organismal and environmental factors. We combined transcriptome-wide gene expression analysis with high-content cellular measurements of neutral lipids to define a core hepatic steatosis MASLD phenotype at baseline (vehicle-treated) and following a lipid challenge. The additive genetic heritability of hepatic steatosis measures was 0.44 (p-value = 0.03) at baseline and 0.42 (p-value = 0.03) at post-lipid challenge. Multivariable linear regression comparing each gene’s expression against hepatic steatosis measures identified 1070 genes at baseline and 1229 genes post-lipid challenge, whose expression showed a transcriptome-wide statistically significant association (standardized |β| ≥ 0.24; Bonferroni-corrected p-value ≤ 0.001) with baseline and post-lipid challenge hepatic steatosis measures, respectively. Functional annotation and pathway enrichment analyses of these genes implicated a broad range of hepatocellular functions, mapping an overall transcriptomic architecture of MASLD-associated steatosis risk in Mexican Americans. The genes whose expression was positively correlated with hepatic steatosis measures suggest a direct role of variation in fatty acid (FA) and cholesterol uptake, de novo lipogenesis (DNL), and carbohydrate shunts in hepatic steatosis risk, as well as a cellular stress-associated and high-turnover metabolic state marked by elevated FA-oxidation and ketogenesis. In contrast, the genes whose expression was inversely correlated with hepatic steatosis measures suggest a significant role of the cellular cytoskeleton, hepatocyte epithelial integrity, and endosomal and autophagic clearance machinery in steatosis risk. Full article

The increasing adoption of electric vehicles (EVs) has created a growing need for battery systems that can deliver high efficiency, reliable operation, and effective thermal management. This study develops an EV battery model using MATLAB/Simulink and Simscape, with the BYD ATTO 3 specifications used as a reference, to investigate the influence of battery voltage, coolant flow, and battery capacity on four key performance indicators: battery temperature, power loss, state of charge (SOC), and state of health (SOH). Response surface methodology (RSM), combined with a Box–Behnken design (BBD) was employed to develop a quadratic regression model, evaluate the significance of the selected factors through analysis of variance (ANOVA), and identify the optimal operating conditions using the desirability function. The simulation results show that battery voltage and battery capacity have a greater influence on electrical performance, whereas coolant flow was more influential in thermal regulation. Under the optimal operating conditions, the battery achieved a temperature of 21.63 °C, a power loss of 3966.30 W, an SOC of 91.37%, and an SOH of 91.34%. The developed regression models demonstrated excellent predictive capability for all response variables, confirming the suitability of the proposed optimization approach. These findings demonstrate that the proposed optimization framework can balance thermal behavior, energy efficiency, and battery health. The developed methodology also provides a practical foundation for battery management and lookup-table-based control strategies in future EV applications. Full article

Improving the performance and lifespan of lithium-ion batteries is a key challenge for the development of electric vehicles. However, accurately estimating the state of charge (SoC), state of health (SoH), and life cycle remains complex due to the electrical, thermal, and aging phenomena associated with these energy storage systems. Against this backdrop, this study proposes a hybrid approach combining an electro-thermal model with a feedforward neural network (FNN) to improve the estimation of key lithium-ion battery performance indicators within a temperature range of 0 °C to 40 °C. The developed methodology was implemented in MATLAB/Simulink and applied to the analysis of the vehicle’s power profile, as well as the evolution of SoC, SoH, and battery life cycle. The results demonstrate an accuracy of 95.3% for state of charge (SoC) estimation, with a mean absolute error of 4.7%. For state of health (SoH) estimation, the accuracy is 95.8% accompanied by a mean absolute error of 4.2%. Lastly, for life cycle prediction, the accuracy is 92.5% with a mean absolute error of 7.5%. The performance results demonstrate the robustness of the proposed approach and its ability to replicate battery dynamic behavior under climatic conditions representative of the African context. This contribution opens up promising avenues for optimizing battery management systems and advancing the sustainable development of electric mobility. Full article

Dynamic inductive charging (DIC) combined with hybrid energy storage systems (HESSs) and vehicle-to-grid (V2G) capabilities offers a promising pathway toward extended-range electric vehicles with grid integration benefits. However, real-time optimal energy management remains challenging due to multi-axis coil misalignment, component aging, and bidirectional power flow uncertainty. This paper proposes an adaptive digital twin driven artificial intelligence (AI) energy management framework integrating physics-informed neural networks (PINNs), soft actor critic (SAC) deep reinforcement learning, and model predictive control (MPC) for optimal power distribution among a proton exchange membrane fuel cell (PEMFC), lithium-ion battery, supercapacitor, dynamic wireless charging, and grid interface in four-wheel drive electric vehicles (4WD-EVs). The framework features: (1) a self-evolving digital twin with online learning via Elastic Weight Consolidation (EWC) updating every 50 cycles; (2) a PINN-based state estimator for battery-state estimation, with an average inference time of 1.1 ms and a worst-case latency of 2.8 ms; (3) a hierarchical SAC–MPC strategy with high-level mode selection and low-level power optimization; (4) real-time five-degree-of-freedom WPT misalignment compensation, achieving a mean efficiency of 91.5% under the evaluated dynamic lateral misalignment conditions, with a 50 mm displacement amplitude; (5) degradation-aware V2G optimization generating €582.50/year in revenue while reducing battery aging by 31.8%; and (6) comprehensive techno-economic analysis yielding a discounted payback period of approximately 5.57 years and a net present value of approximately €3777 over a 10-year horizon. Validated through 200+ hours of hardware-in-the-loop (HIL) simulation on the dSPACE/NVIDIA Jetson platform, the proposed approach achieves a 24.3% cost reduction and 31.8% lower battery degradation. The MPC controller exhibits an average execution time of 32.1 ms, a 95th-percentile latency of 44.8 ms, and a worst-case latency of 62.4 ms, while remaining within the 100-ms real-time control deadline. Results demonstrate the viability of adaptive digital twins for next-generation EVs with autonomous charging and multi-source architectures. Full article

Directional asymmetry and Dutch-roll are critical phenomena observed during powered parafoil vehicle (PPV) flight tests, complicating stable flight and leading to obvious lateral–directional biases in autopilot tracking. However, existing PPV models typically neglect propeller counter-torque (PCT), resulting in limited research on directional asymmetry. Moreover, conventional Newton–Euler formulations require explicit treatment of internal constraint forces, complicating analytical linearization and local stability analysis. To address these issues, first, this paper develops a nonlinear 9-degree-of-freedom (9-DOF) PPV model using Kane’s equations with quasi-velocities. This multibody dynamic model provides a compact and structurally consistent basis for linearization and stability analysis. Subsequently, the mechanism analysis shows that PCT shifts the coupled roll–yaw equilibrium and is the primary physical source of the observed directional asymmetry. In addition, the Dutch-roll mode is identified as the dominant oscillatory mode governing the lateral–directional stability of the PPV. The modal analysis further indicates that increasing thrust and directional control inputs reduce the Dutch-roll damping ratio. On this basis, a damping-ratio-based flight envelope is constructed. Furthermore, numerical simulations and flight-test comparisons demonstrate that the proposed model captures the principal PPV dynamic responses. The simulation results also support the mechanism analysis of directional asymmetry and the Dutch-roll. Full article

Against the backdrop of the “dual-carbon” strategy (carbon peaking and carbon neutrality), countries worldwide are committed to advancing the application of new energy in the transportation sector. This has spurred the rapid development of electric vehicles (EVs) and led to higher requirements for the research and construction of charging infrastructure. To address the challenges of high daily power purchase costs and severe grid-connected power fluctuations in the daily scheduling of PV–storage–charging integrated stations, as well as the limitations of conventional particle swarm optimization (PSO) with random or chaotic initialization—including insufficient engineering prior knowledge of station time-of-use (TOU) electricity prices and energy storage state of charge (SOC), numerous inferior solutions in the initial population, and high susceptibility to premature convergence—this paper develops a dual-objective optimal scheduling model that balances daily power purchase cost and grid-connected power fluctuation. The model integrates PV output, EV charging loads, energy storage charge–discharge schedules, and multiple categories of operational constraints. Grounded in the economic operation principle of “valley-period charging and peak-period discharging”, an improved PSO algorithm with electricity PriceSOC joint guided initialization (PriceSOC-PSO) is proposed. High-quality initial particles are generated by setting segmented SOC targets, introducing random perturbations, and implementing closed-loop correction of the energy storage schedule, while hybrid random particles are incorporated into the population to preserve diversity. Multiple simulation scenarios, including the no-energy-storage case, standard PSO, chaotic-initialized PSO, the proposed PriceSOC-PSO, Grey Wolf Optimizer (GWO), Harris Hawks Optimization (HHO), and the Sparrow Search Algorithm (SSA), are established to carry out objective weight sensitivity analysis and cross-algorithm comparative analysis. The results demonstrate that, compared with the no-energy-storage scenario, the proposed strategy reduces the daily power purchase cost and grid-connected power fluctuation by 11.7% and 74.9% respectively under the weight configuration $({\omega }_1=0.3, {\omega }_2=0.7)$. When the weight configuration is adjusted to (${\omega }_1=0.7, {\omega }_2=0.3)$, the two indicators are decreased by 14.8% and 62.6% respectively. Full article

High wind-power penetration increases balancing requirements in integrated energy systems (IESs), while solvent-storage-assisted carbon capture power plants (CCPPs) and electric vehicle (EV) aggregators provide complementary flexibility at different timescales. This paper proposes an electric–carbon dual time-shift coordinated dispatch approach coupling carbon-energy shifting with vehicle-to-grid (V2G) electrical-energy shifting. First, a reduced-order model represents the dominant thermal inertia and short-term response of solvent regeneration. Second, EV availability uncertainty is characterized by Monte Carlo sampling, with quantile-based power and mobility-energy envelopes incorporated into aggregate SOC and mobility constraints together with a throughput-based battery-degradation cost. Finally, a 15-min mixed-integer linear programming model integrating power-to-gas, hydrogen-blended combined heat and power, thermal storage, and tiered carbon trading is solved using CPLEX. Compared with the baseline, the proposed coordinated dispatch strategy reduces operating cost from USD 77.19 × 10⁴ to 58.65 × 10⁴, net carbon emissions from 5841.71 to 2742.46 tCO₂, and the wind-curtailment rate from 42.98% to 1.15%. Specifically, relative to the same system without EV–V2G coordination, incorporating EV–V2G further reduces operating cost and net carbon emissions by 0.93% and 3.93%, respectively, while lowering the wind-curtailment rate from 4.23% to 1.15%, corresponding to a 72.8% relative reduction. Frequency-band analysis shows that the CCPP and electrolyzer provide the two largest contributions to low-frequency balancing, at 42.85% and 30.02%, respectively, whereas EV–V2G and CHP provide the two largest contributions to higher-frequency balancing, at 45.37% and 23.71%, respectively. The main limitations are the reduced-order regenerator model, fleet-level EV aggregation without distribution-network constraints, and fixed equipment capacities. Full article

The adoption of electric vehicles (EVs) is surging globally as more EV units are bought year on year. As many EVs reach their end of life (EoL), there is a high probability that traction EV motors are still in working condition. Therefore, in this paper, the technical considerations around the repurposing of EoL EV motors for microgrid applications in sub-Saharan Africa (SSA) are discussed. The circular economy (CE) approach can help reduce the environmental impact of producing EV motors, and it can also enable renewable energy power generation in SSA, all of which closely align with sustainable development goals (SDGs) 7, 12, and 13. Firstly, commonly used motor topologies used in EVs are highlighted and differentiated by their features. Potential second life microgrid applications are outlined with off-grid wind power generation identified as being particularly suitable for the repurposed motors. Applicable electrical setups are discussed, after which electromagnetic performances of three selected EV motors in wind turbine environments are evaluated using finite element analysis (FEA) models. Finally, technical challenges facing the redeployment of these EV motors are discussed with recommendations for effective project implementations. Full article

Vehicular accidents are among the leading causes of fatalities, economic losses, and traffic disturbances worldwide. Understanding the intricacies of accident emergence and propagation is crucial for devising mitigation strategies. Among the different analysis approaches, cellular automata modeling stands out as a powerful tool since it enables the reproduction of complex collective dynamics through simple local interactions. Although numerous CA-based studies have addressed traffic accidents under different conditions, the literature still lacks a comprehensive synthesis dedicated to cellular automata-based vehicle-to-vehicle accident modeling. This systematic review classifies and comparatively analyzes how cellular automata models represent vehicle-to-vehicle collision mechanisms, dangerous traffic states, accident occurrence, and accident-related traffic effects. Through searches in Scopus, Web of Science, TRID, and IEEE Xplore, 740 records were identified and subsequently filtered to 90 papers according to the PRISMA methodology. The selected studies were classified according to a common taxonomy that includes rear-end, head-on, and side-impact collisions, as well as lateral conflicts. The contribution of the proposed taxonomy is threefold: to provide a deeper understanding of the key modeling principles, to review the types of accidents most frequently analyzed, and to identify current research gaps. Overall, this paper serves as a reference point for developing, comparing, and extending cellular automata models for traffic safety analysis. Full article

To address the high computational cost of full-domain transient simulations of heat pipe-assisted phase change energy storage systems for vehicle engine exhaust waste heat recovery, this paper proposes a space-time dual decoupling method. Dimensional analysis reveals significant time-scale disparities between the fast and slow sub-domains of the system, on the basis of which the full-domain transient model is sequentially decoupled into a steady-state calibration of the fast sub-domain and a transient evolution of the slow sub-domain. Multiple steady-state calculations are first performed to calibrate an explicit mapping between the recovered heat power and the wall temperature; this mapping is then imposed as a time-varying heat-flux boundary condition to drive the independent transient evolution of the heat storage process. The results show that the fast sub-domain reaches a quasi-steady state within 20 s, with the energy balance deviation decreasing from 38.5% to below 5%. The predicted heat pipe wall temperatures and recovered heat powers agree closely with those of the traditional full-domain transient method, with maximum relative errors of 1.2% and 5.54%, respectively, while the total computational time is reduced from 4364.54 h to 147.99 h, corresponding to an approximately 29.5-fold improvement in efficiency. Full article

Hydrodynamic models underpin Autonomous Underwater Vehicle (AUV) design, motion control, and performance evaluation. Existing methods face two critical bottlenecks: (1) conventional explicit CFD requires predefined trajectories, which fails to capture true motion responses under combined rudder-propeller action and creates a disconnect between simulation and real operations; (2) the widely adopted Standard Submarine Motion Equations (SSME) suffer from high parameter redundancy, while high-precision non-parametric models incur prohibitive computational costs, hindering embedded deployment. To address these gaps, this paper proposes an implicit CFD-driven framework for fully appended AUVs equipped with through-body thrusters. It requires no preset trajectories, directly coupling periodic propeller thrust and rudder angle excitations to achieve 5-degree-of-freedom (5DOF) spatial motion simulations aligned with real navigation states. Parametric and non-parametric models are identified via Least Squares (LS) and Neural Networks (NN), respectively. Sobol global sensitivity analysis reduces SSME dimensionality, yielding a Basic Submarine Motion Equation (BSME) with only 25 key parameters—cutting the parameter count by 55% with negligible accuracy loss. Validation shows the non-parametric NN model reduces prediction error by over 10% compared to its parametric counterpart, while the streamlined BSME enables real-time forecasting in low-power computing scenarios. This approach balances accuracy and efficiency for rapid hydrodynamic prediction during early AUV design and embedded controller deployment. Full article

To meet the demands for lightweight design and high-conductivity connections in new energy vehicles, the high-quality joining of dissimilar Al/Cu metals has emerged as a critical research focus. In this study, laser welding was performed on 2 mm-thick 1060 pure aluminum and T2 copper plates. The effects of laser power (3.6–4.0 kW) and welding speed (0.9–1.5 m/min) on the interfacial microstructural evolution and mechanical properties of the lap joints were systematically investigated. The results demonstrate that the macroscopic morphology of the weld is primarily governed by heat input: excessive laser power induces transverse cracking, whereas an overly low welding speed promotes porosity. Microstructural analysis revealed that intermetallic compounds (IMCs), such as Al₂Cu, AlCu, and Al₄Cu₉, predominantly form at the interface, with their morphology and distribution varying significantly depending on the heat input. Under the optimized parameters of a 3.8 kW laser power and a 1.2 m/min welding speed, sufficient mixing of the molten Al and Cu was achieved. This promoted the formation of fine, dispersed IMCs accompanied by a continuous Al–Cu eutectic layer at the interface, yielding a maximum tensile-shear load of 1561 N. This research elucidates the intrinsic relationship between heat input and the microstructure–property correlation of Al/Cu laser-welded joints, identifying a viable process window for 2 mm-thick sheets and providing theoretical and practical guidance for joining dissimilar medium-thickness metal plates. Full article

The growing adoption of electric vehicles (EVs) and the increasing need for coordinated charging and energy management have highlighted the importance of vehicle-to-everything (V2X) technologies within battery management systems (BMSs). However, existing studies often treat EVs as idealized storage systems, overlooking battery and BMS-related operational constraints, and typically analyze driving, charging, and bidirectional energy exchange in isolation, limiting realistic, end-to-end evaluation of daily operation scenarios. This paper addresses these gaps by analyzing how advanced, BMS-integrated V2X capabilities can be deployed in real-world EV operation, focusing on battery utilization, operational performance, and system-level energy interactions. A unified, scenario-based methodology combines mobility demand, AC/DC charging behavior, and bidirectional V2X services within a single daily operational framework. Representative use cases for both passenger EVs and electric light commercial vehicles (eLCVs) are developed to capture realistic driving patterns, environmental conditions, and energy exchange scenarios. The results indicate that V2X operation can provide substantial gross economic value in the investigated scenarios. For the eLCV cases, the estimated increase in equivalent full cycle (EFC) throughput rate ranges from approximately 14.3% to 30.3%, while combined summer–winter cumulative avoided electricity purchase cost reaches approximately EUR 4033 for the higher-power charging strategy, equivalent to 57.0% of the adopted battery cost reference. The analysis also highlights the strong influence of ambient temperature and usage patterns on energy consumption, charging strategies, and overall system performance. Overall, this work provides a holistic and practical evaluation framework for V2X-enabled BMS operation, demonstrating its potential to improve grid support, enhance energy efficiency, and support sustainable EV integration while balancing economic and battery-lifetime trade-offs. Full article