This work represents the development of a Fuel Cell Hybrid Electric Vehicle (FCHEV) model, its validation, and the comparison of different control strategies based on the Toyota Mirai (1st generation) vehicle and its subsystems. The main investigated parameters are hydrogen consumption, and the variation of the state of charge, current, and voltage of the battery. The FCHEV model, which is made up of multiple subsystems, is developed and simulated in MATLAB^® Simulink environment using a rule-based control strategy derived from the real system. The results of the model were validated using the experimental data obtained from the open-source Argonne National Laboratory (ANL) database. In the second part, the equivalent consumption minimization strategy is implemented into the controller logic to optimize the existing control strategy and investigate the difference in hydrogen consumption. It was found that the ECMS control strategy outperforms the rule-based one in all drive cycles by 0.4–15.6%. On the other hand, when compared to the real controller, ECMS performs worse for certain considered driving cycles and outperforms others.

One of the most effective methods to limit the mechanical vibrations of bladed disks is the use of friction damping at mechanical joint interfaces. Unfortunately, dedicated tests to assess the impact of mistuning and the effectiveness of friction dampers are uncommon. This paper presents an original design of an academic demonstrator to perform an experimental analysis of the dynamic response of a tip-free bladed disk with under-platform dampers (UPDs), including an identification of intrinsic and contact mistuning introduced by the UPDs. The 48-blade disk was tested in a vacuum spinning rig by using permanent magnets. Vibration measurements were performed with the Blade Tip-Timing system. Tests were simulated using the Policontact tool, which predicted the average experimental nonlinear response in the presence of UPD, confirming the tool’s ability to capture the general nonlinear dynamic behavior of the mistuned bladed disk. This study presents a novel approach combining experimental Blade Tip Timing (BTT) with numerical simulations using Policontact (ver. 3.0) software and a model update based on experimental evidence to validate nonlinear dynamic responses. It distinguishes between intrinsic and contact mistuning effects, providing new insights into their impact on bladed disk vibrations. Additionally, a comparison of aluminum and steel UPDs reveals that steel offers a 26% greater damping efficiency due to its higher density and preload, significantly improving vibration reduction.

This study discusses the role of the equivalence factor and penalty function in improving the performance of energy consumption minimization strategies in Range Extended Electric Vehicles (REEVs). In conventional ECMS, equivalence factors are typically derived from constant efficiency assumptions for simplicity or adaptively adjusted according to driving conditions in adaptive ECMS. In REEVs, however, the battery efficiency exhibits nonlinear behavior in the low SOC range, which directly leads to variability in the equivalence factor within conventional ECMS. This study investigates the influence of the variable equivalence factor on the overall fuel economy. The equivalence factors are usually considered constant or vary adaptively depending on driving cycles. However, the variation in battery efficiency is often neglected. The present study compares the results obtained for both constant and variable battery efficiencies in deriving the equivalence factors. The simulation results show that an improvement of approximately 3% in fuel economy was obtained for UDDS, NEDC, and WLTC driving cycles as a result of applying the variable equivalence factor. Additionally, through an analysis of various penalty function designs, the study highlights their crucial role in optimizing fuel consumption across different driving cycles.

Contact-induced mistuning at the blade–disk interface is a major source of variability in the dynamic response of bladed disks, yet its identification remains challenging due to the intrinsic complexity of the contact phenomenon. Contact mistuning in this framework is defined as the variation of the contact topology at the blade–disk interface, i.e., the spatial distribution of contacting and non-contacting regions governing the mechanical interaction between components. This work proposes a data-driven framework for the identification of contact mistuning based on a reduced set of experimentally measured maps. A statistical representation of the contact space is constructed using Principal Component Analysis. This representation is subsequently exploited to generate physically consistent synthetic contact patterns. These are combined with blade intrinsic mistuning parameters to build a dataset linking contact conditions and blade variability to the natural frequencies of a disk–one-blade assembly. A k-nearest neighbors algorithm is employed for inverse identification using a subset of measured natural frequencies. The identification relies on modes selected through sensitivity analysis, and robustness is improved using a weighted distance metrics. The methodology is validated using both a reduced-order model based digital twin and experimental data. The results show accurate identification of contact patterns, confirmed through comparison of modal properties of the full mistuned assembly. Therefore, the proposed framework provides a practical tool for the experimental identification of contact mistuning in bladed disks.

The performance of battery electric vehicles (BEVs) is significantly affected by ambient temperature, especially in areas with substantial seasonal fluctuations. This study examines the influence of Uzbekistan’s continental climate on electric vehicle range, battery degradation, and operational expenses through the development of a MATLAB/Simulink-based electro-thermal vehicle model. Three representative BEVs were selected for the simulation study: the Nissan Leaf (24 kWh), the Chevrolet Bolt EV (60 kWh), and the 2017 Volkswagen e-Golf (35.8 kWh, DC fast-charging version). The modeling framework combines longitudinal vehicle dynamics, battery behavior that changes with temperature, HVAC auxiliary loads, and range data that has been tested in controlled laboratory campaigns. The model was validated using open-source test results at different ambient temperatures, and the measured data matched the model very closely. Using validated temperature–range relationships, monthly climatic data for Uzbekistan were applied to estimate annual charging frequency for a representative annual driving distance. Results indicate that real climatic operation increases annual charging demand compared to moderate-condition OEM expectations. This increase not only affects electricity expenditure but also accelerates battery cycling intensity. A piecewise multiplicative degradation model was therefore implemented to quantify capacity loss under climate-adjusted cycling conditions. The findings demonstrate that continental temperature extremes significantly influence both short-term vehicle efficiency and cycle-induced battery capacity degradation. The proposed framework provides a climate-aware predictive tool for realistic performance estimation and supports evidence-based planning for electric mobility deployment in regions with pronounced seasonal variability.

Background: Experimental modal analysis with laser Doppler vibrometry is usually limited to the identification of mono-dimensional mode shapes, typically in the out-of-plane direction. This simplification restricts the understanding of the true three-dimensional deformation of a structure. Objective: The objective of this work is to develop an experimental–computational framework for the reconstruction of full three-dimensional mode shapes using a single scanning laser Doppler vibrometer and to establish a consistent procedure for comparison with numerical mode shapes expressed in different coordinate systems. Methods: The methodology combines frequency response function measurements obtained from three independent orientations of the tested component while maintaining a fixed laser head position. A reconstruction algorithm based on projection relations and rotation matrices is employed to recover the complete three-dimensional vibration shape, while an additional least-squares-based projection algorithm is introduced to project numerical mode shapes into the experimental reference frame. The methodology is experimentally validated on an air-compressor collector using a Polytec PSV-500 scanning laser Doppler vibrometer. Results: The reconstructed experimental three-dimensional mode shapes showed strong agreement with numerical predictions for the first three vibration modes, with Modal Assurance Criterion values of 0.93, 0.94, and 0.85, confirming strong modal correspondence. Conclusions: The proposed framework provides a cost-effective and flexible alternative to multi-head vibrometer systems for three-dimensional experimental modal analysis and enables reliable reconstruction of three-dimensional mode shapes together with consistent comparison with numerical models.