With the increasing demand for high-rise rescue equipment, lightweight design of large aerial fire trucks is important for reducing structural redundancy while maintaining safety. This study aims to improve the lightweight design of the turntable in a 63 m hybrid-boom aerial fire truck by using a finite element analysis (FEA) and intelligent-optimization procedure validated by real-vehicle testing. A finite element model of the DG63 eight-section boom-turntable system was established, and static and modal analyses were performed under three representative working conditions: maximum working height, maximum working outreach, and 49 m operation. A real-vehicle stress test using a wireless dynamic strain testing system was then conducted to validate the finite element model. The turntable was selected as the optimization target because of its critical role in structural safety and the mass reduction potential indicated by the preceding FEA and real-vehicle test results. Sensitivity analysis was used to identify six plate-thickness design variables. Least squares regression, support vector regression, and particle swarm optimization-back propagation (PSO-BP) neural network models were compared for response prediction, and an improved genetic algorithm (GA) was then used for constrained mass minimization. After optimization, the turntable mass was reduced from 1.447 t to 0.995 t, corresponding to a 31.2% reduction. The maximum stress and deformation of the optimized turntable were 163.4 MPa and 2.292 mm, respectively, which were below the allowable stress of 172.5 MPa and deformation limit of 60 mm. The predicted mass was 0.986 t, with an absolute error of 0.009 t and a relative error of 0.9%. These results demonstrate that the proposed method can reduce the mass of the turntable while satisfying strength and stiffness requirements, and can provide a reference for lightweight design of key load-bearing structures in large aerial fire trucks.
As a fluid transmission device, the hydrodynamic torque converter (HTC) achieves flexible transmission through energy conversion between impeller cascades and fluid. High-speed, high-power operating conditions and highly twisted cascade systems lead to highly developed three-dimensional (3D) turbulence and intense cavitating phase-change flow inside the HTC. Thus, improving hydrodynamic performance and suppressing cavitation have become key aspects of high-performance HTC design. The turbine blade, the most twisted one in the cascade system, largely determines the fluid flow direction in the HTC. Based on this, this paper investigates the effects and control mechanisms of key turbine blade parameters on HTC cavitation and hydrodynamic performance. First, it proposes to adopt catenary and cycloid curves for turbine blade parametric design, deriving the relationship between blade control parameters (leading/trailing edge angles, peak position and height of the camber line, peak position and height of blade thickness) and the two curves. Then, it analyzes the impacts of turbine camber line parameters (deflection angle, peak position and height of turbine camber line) on HTC hydrodynamic and cavitation performance. Finally, it clarifies the regulatory mechanisms of these key parameters from the perspective of the internal flow field. Results show that increasing the camber line peak position and height reduces the stall torque ratio ( TR 0 ), pump torque coefficient ( λ B0 ) and cavitation volume of the HTC. This is because the change induces significant flow separation at the turbine leading edge and stator suction side, elevates local static pressure, reduces meridional velocity and pump inlet-outlet dynamic pressure increment, and finally decreases TR 0 and λ B0 while effectively suppressing HTC cavitation. In addition, the increase of the deflection angle of the camber line will lead to the suppression of cavitation. The proposed turbine blade parametric design and cavitation suppression method provide a reference for high-performance turbomachinery design.
This article proposes the architecture of a solar-powered grid integrated on-board charger for electric vehicles (EVs) utilizing the unique combination of an interleaved Cuk converter and a modified flyback converter. On one side, the interleaved Cuk converter ( IL-Cuk ) power factor corrected (PFC) converter outstandingly satisfies the grid side necessities; whereas, the flyback converter on the other side confirms ripple (twice the line frequency ripples) free charging of the EVs under different charging modes. The discontinuous conduction mode (DCM), which is intended for use by the IL-Cuk converter, has inherent benefits like zero current switching and the elimination of losses because of reverse recovery in diodes. Due to its DCM features, this operating mode also leads to smaller PFC inductors and fewer sensors. The developed charger’s dependability is enhanced by using both the grid and the solar PV (SPV) array for charging. The execution of the developed charger is assessed across a gamut of operating situations in order to determine its compatibility with renewable energy sources such as solar photovoltaic (SPV) array. In modern transportation, battery electric cars (EVs) using solar power are becoming more and more popular. The developed topology has been experimentally verified through real-time hardware implementation using an OPAL-RT platform, demonstrating that the obtained real-time results closely correspond to practical experimental performance under both grid-supplied and solar-powered operating conditions. The proposed 2.4 kW charger maintains a regulated DC-link voltage of 300 V and charges the battery at a constant current of 10 A during both grid-only and grid-assisted solar charging modes. The proposed charger achieves a peak efficiency of 92.75%, operates at nearly unity power factor, and restricts the grid current THD to 0.3% under rated conditions and below 2% during dynamic grid voltage variations, thereby complying with the IEC 61000-3-2 power quality requirements.
As the core energy-replenishing unit of series hybrid electric vehicles (SHEVs), the range extender experiences inherent engine torque pulsations during start-stop and steady-state operating conditions. These pulsations induce intense lateral-torsional-pendulum (LTP) coupled vibrations through the mounting system, emerging as a critical bottleneck restricting overall vehicle NVH performance. Conventional isolated analysis methods for crankshaft torsional vibration and mounting isolation, alongside existing active torsional control strategies, frequently suffer from frequency-domain coupling conflicts between power generation and vibration attenuation. To address these issues, this study investigates an active torsional vibration suppression strategy for a specialized vehicle range-extender-mounting system, grounded in the underlying coupled vibration mechanism. First, a multi-degree-of-freedom (MDOF) lumped-parameter dynamic model of the system is established to elucidate the LTP coupled vibration mechanism, verifying the dynamic law that suppressing pure torsional vibration can synchronously mitigate lateral and vertical vibrations. Subsequently, based on a dual-loop motor control method oriented toward torsional vibration control, a parallel narrowband Filtered-x Least Mean Square (FxLMS) active torsional vibration controller with adjustable attenuation intensity is designed to specifically target the second and fourth dominant engine harmonic excitations. Finally, the performance of the proposed strategy is validated through numerical simulations and hardware-in-the-loop (HIL) semi-physical experiments. The results indicate that the proposed strategy achieves a maximum torque vibration amplitude attenuation of Change to 78.5% under steady-state conditions while maintaining superior convergence and robustness under dynamic variable-speed conditions. Concurrently, the lateral and vertical vibration amplitudes of the mounting system during start-stop phases are synchronously reduced by over 50%, effectively realizing the co-optimization of active torsional control and system-level NVH performance.
Rare but high-risk multi-vehicle interactions are essential for evaluating the safety boundaries of autonomous driving systems, yet are difficult to cover efficiently through naturalistic replay, single-agent perturbation or manual parameter combinations. This study develops a high-value multi-vehicle scenario generation framework based on SMART target-domain adaptation. Two representative highway scenarios, constrained cut-in and zipper merge, are mined from highD and exiD using event-triggered rules and converted into a unified multi-agent scene representation. A SMART-based generative Transformer discretizes vehicle motions and road structures into motion and road tokens, is pretrained on WOMD, and is fine-tuned on the mined target-domain samples. During inference, Top-p sampling generates multimodal motion-token sequences, while physical-consistency filtering after continuous trajectory reconstruction rejects or resamples candidates with abnormal acceleration and jerk. Generated scenarios are evaluated across trajectory, scenario and system-response levels through open-loop quality assessment, target-scenario construction and closed-loop validation with an IDM + Pure Pursuit SUT. Compared with CS-LSTM, the proposed method reduces minADE and minFDE by 12.7% and 12.8%, respectively, and improves map compliance to 99.10%. Construction success reaches 42.0% for constrained cut-in and 35.6% for zipper merge. Selected critical scenarios reduce the SUT pass rate from 86.7% to 20.0% and expose collision and motion-paralysis failures. These results demonstrate map-compliant, replay-compatible and testing-oriented high-value interaction scenarios for operational-boundary evaluation and safety validation of autonomous driving systems within the validated highway domains.
Distributed-drive electric vehicles (DDEVs) tend to experience significant lateral instability when operating under high-speed and low-adhesion conditions, posing serious challenges to driving safety. To address this issue, this study proposes a coordinated control framework integrating Active Front Steering (AFS) and Direct Yaw Moment Control (DYC), combined with a RIME-enhanced quadratic programming (RIME-QP) torque distribution strategy. A phase-plane-based coordination mechanism is introduced to categorize vehicle dynamic states into stable, critical, and unstable regions, allowing adaptive allocation of control authority between AFS and DYC. At the upper control layer, a hybrid structure combining model predictive control (MPC) feedforward and iterative linear quadratic regulator (iLQR) feedback is developed to enhance robustness against model uncertainties and nonlinear tire effects. At the lower layer, the torque allocation problem is formulated as a convex quadratic programming problem and solved using the RIME-enhanced algorithm to improve optimization robustness under complex constraints. Hardware-in-the-loop (HIL) experiments under multiple driving scenarios demonstrate that the proposed strategy significantly improves yaw rate and sideslip tracking performance while maintaining real-time computational feasibility.
How to address the challenges in human–machine cooperative control of intelligent vehicle is still an important issue, which include the conflicting objectives between driver and controller, the difficulty in fusing multi-source risk information from both vehicle and driver states, the limited robustness of conventional risk estimation under uncertain interference. To this end, this paper proposes a human–machine collaborative driving collision avoidance control based on Bayesian risk estimation. Firstly, a Bayesian multi-factor driving risk estimation model is designed by integrating time-to-collision, collision distance and driver fatigue state. Simultaneously, the risk level is used as controller weights within the optimization objective of the non-cooperative game-theoretic controller for resolving conflicting objectives between the driver and the controller. Finally, the effectiveness of the proposed method is verified using PreScan and Simulink co-simulation, along with a hardware-in-the-loop driver testing platform. Experimental results show that under single and double-lane-change scenarios, the proposed strategy exhibits strong disturbance resistance in the white noise and fast response, significantly improving collision avoidance performance in complex environments.
Heavy mining dump trucks are subjected to severe road excitation and large payload variations, which make it difficult for conventional passive hydro-pneumatic suspensions to achieve satisfactory ride performance. To address this issue, this study proposes a mode-switching hydro-pneumatic suspension with four discrete stiffness-damping modes realized by the coordinated switching of two accumulators and two throttling branches. A nonlinear dynamic model of the suspension is established by considering gas compression, chamber pressure variation, throttling flow, check-valve switching, and friction. To evaluate the mode-dependent vertical ride performance, the suspension model is integrated with an effective road-input model considering tire enveloping and a two-degree-of-freedom quarter-car model in an AMESim-MATLAB/Simulink co-simulation framework. Static and dynamic bench tests are carried out to validate the model. The results show that the proposed suspension exhibits clear nonlinear and mode-dependent dynamic characteristics, enabling staged adjustment of equivalent stiffness and damping. The quarter-car-based simulations indicate that the influence of suspension mode is more significant under full-load conditions. Under severe road excitation, the soft-stiffness/high-damping mode provides the best vibration-isolation performance. The experimental results agree well with the predicted overall trends, confirming the validity of the proposed model. This study provides a practical suspension concept and a validated modeling framework for ride-oriented design of hydro-pneumatic suspensions for heavy-duty mining vehicles.
To completely eliminate the issues associated with flat tires and deflation, non-pneumatic tires, as a novel safety tire technology, have garnered increasing attention. Nevertheless, owing to their distinctive structure and materials, non-pneumatic tires can exhibit vibration responses that differ from those of traditional pneumatic tires when subjected to external damage. Therefore, it is of great significance to explore an effective method for identifying damage in non-pneumatic tires to guarantee vehicle driving safety and the performance of non-pneumatic tires. In this paper, a damage identification method for intelligent non-pneumatic tires based on wavelet packet energy is presented. First, a three-dimensional nonlinear finite element model of a non-pneumatic tire under damage conditions was established, and dynamic simulation analysis was conducted. Second, vibration signal data of non-pneumatic tires in different damage states were collected, and wavelet packet energy characteristics were extracted. Finally, a damage sensitivity index based on the change rate of the total wavelet packet energy was established to achieve the monitoring and diagnosis of the health status of non-pneumatic tires. The research findings indicate that the proposed method can accurately identify the damage status of non-pneumatic tires without the necessity of complex parameter estimation and model verification procedures. The research results offer a new perspective for the health monitoring and damage recognition of non-pneumatic tires.
The escalating demand for increased torque and power density in electric drive systems for new-energy trucks intensifies the technical challenges associated with vibration, noise, and dynamic reliability. This study establishes a high-fidelity electro-mechanical coupling model for an integrated e-drive, combining a vector-controlled interior permanent-magnet synchronous motor with a two-stage gearbox. The model incorporates electromagnetic torque ripple, time-varying mesh stiffness, gear backlash, and transmission error. Bench tests under multiple operating conditions provide validation data. Global sensitivity analysis identifies input shaft stiffness and gear mesh parameters as most influential. These parameters are then updated via Particle Swarm Optimization, effectively curtailing simulation errors for key dynamic responses from over 20% to less than 8%. The updated model elucidates critical coupling dynamics: dynamic loads surge to 2.5 times nominal during rapid acceleration; severe impacts occur during gear re-engagement in regenerative braking; and a pronounced mid-frequency whine arises from modulation between electromagnetic and mechanical excitations. The validated high-precision framework offers a robust foundation for the design, optimization, and NVH mitigation of next-generation electric drive system.
Accurate yaw angle information is essential for lateral stability and path tracking control of autonomous vehicles. Unlike actuator faults, GNSS-based yaw faults directly corrupt feedback states and can degrade vehicle dynamics, particularly at high speeds. In practice, GNSS yaw measurements are frequently affected by bias and drift caused by multipath effects, signal blockage, and degraded positioning conditions. This study proposes a control-oriented fault detection and reconstruction framework based on an adaptive sliding mode observer (ASMO) integrated with a lateral dynamic error model. The proposed ASMO employs an extended four-state error representation to maintain structural consistency between the observer and the path tracking controller. A multi-state adaptive injection structure adjusts the observer gain according to estimation errors, thereby reducing the oscillatory behavior of conventional sliding mode observers while maintaining robustness against nonlinear tire behavior and model uncertainty. In addition, a complementary fault detection strategy combining prediction-based feasible bounds and drift monitoring is developed to detect both abrupt bias faults and incipient drift faults. The reconstructed yaw signal is directly used in the control loop to support continuous fault-tolerant operation without controller switching. The proposed framework was validated through 100 km/h double lane change simulations in MATLAB/Simulink using a nonlinear vehicle model. Under a severe 5 ° GNSS yaw fault, the ASMO reduced the RMS lateral distance error from 0.3079 to 0.0401 m, corresponding to an 86.99% reduction compared with the uncompensated fault case. Although the RMS heading angle error slightly increased from 0.0427 to 0.0473 rad due to corrective steering behavior, the maximum lateral deviation was reduced from 0.4924 to 0.1761 m, corresponding to a 64.23% reduction, while maintaining a positive lane-boundary margin. These results demonstrate that the proposed ASMO enhances lateral fault-tolerant performance and safety-oriented functional safety in GNSS-challenged environments.
This study investigates the lane-change response of a tractor–semitrailer vehicle and determines the corresponding geometric operating boundaries in terms of steering and velocity parameters. The main contributions are the development of a 28-degree-of-freedom multi-body dynamic model of a four-axle articulated vehicle using the Newton–Euler formulation, its low-frequency experimental assessment under representative low-to-moderate lateral-excitation conditions, its subsequent application in numerical parametric simulations to identify geometric lane-change operating boundaries in the velocity–steering parameter space. These boundaries are derived by post-processing the complete simulated swept envelope and are interpreted as condition-specific numerical predictions rather than experimentally validated thresholds of dynamic instability. The model incorporates tire nonlinear characteristics, suspension dynamics, and articulated coupling effects. A sinusoidal steering input is employed to simulate lane-change maneuvers under low-adhesion road conditions. The influences of steering amplitude, steering frequency, and vehicle speed on trajectory deviation and lane-change dynamic response are systematically analyzed. The results provide condition-specific numerical references for evaluating the swept-envelope response of articulated vehicles under prescribed steering inputs and offer a basis for future studies involving warning, monitoring, and control-oriented applications.
Thermal runaway of power batteries is a critical bottleneck restricting the development of electric vehicles. To improve the safety and operational reliability of electric vehicle power batteries, this study proposes a battery thermal management model with an air-liquid coupled heat dissipation structure and conducts optimization design and predictive analysis on its thermal performance characteristics. Firstly, a novel air-liquid coupled heat dissipation model is constructed with 21700 lithium-ion batteries as the research object. Then, numerical analysis of the temperature field characteristics of the battery heat dissipation model is performed based on fluid mechanics theory. The influences of structural and operating parameters, including the contact angle between the battery and aluminum tube, coolant flow direction, coolant flow rate, and cooling air velocity, on the heat dissipation performance are investigated via orthogonal experiments. Subsequently, the TOPSIS method is employed to comprehensively evaluate multiple heat dissipation performance indicators, and the overall performance of each design scheme is determined according to normalized scores. Finally, a BP neural network prediction model for the heat dissipation characteristics of the model is established to realize high-efficiency prediction of the scheme’s heat dissipation performance. The results show that Orthogonal Scheme 16 achieves a maximum battery temperature of 30.02°C and a maximum temperature difference of 4.54°C, both satisfying the temperature limit constraints for power battery modules. The normalized scores calculated by the TOPSIS method can accurately reflect the relative merits of each experimental scheme. In addition, the BP neural network presents obviously higher prediction accuracy than the multiple linear regression model, with reliable and precise prediction performance.
Driving style classification in simulator-based studies is substantially confounded by the motion cueing washout algorithm, which independently alters the driver’s neuromuscular and cognitive responses. This paper quantifies this confound and proposes a condition normalisation procedure using driving simulator data. The dataset includes N = 28 participants (23 males, five females; mean age 29.0 ± 4.6 years; licence experience 10.0 ± 3.4 years) completing seven washout conditions—Classic (C), MRAC-A1/A2, Hybrid-H1/H2, LQR-O1/O2—in a within-subject counterbalanced design, with simultaneous recording of (i) 13-channel upper-limb surface EMG at 11.2 Hz, (ii) binocular pupillometry at 60 Hz, (iii) six-DOF motion platform signals at 20 Hz, and (iv) vehicle IMU dynamics at 20 Hz. Four modality-specific feature vectors were constructed: 106 EMG features (RMS, spectral onset index, CCI, bilateral ratio), seven pupillometric features (mean diameter, SD, blink rate, band powers), 12 platform features (acceleration RMS per axis, sway–yaw Lissajous coupling index), and 14 vehicle dynamics features (lateral acceleration statistics, yaw rate RMS, spectral band powers). Within-subject z -score normalisation removes the washout main effect from the physiological feature space. Support Vector Machine, Random Forest, and XGBoost classifiers were evaluated under leave-one-out cross-validation on raw features and normalised residuals. XGBoost achieved F 1 = 0.891 on condition identification and F 1 = 0.712 on driving style from normalised features, compared to F 1 = 0.431 without normalisation. SHAP attribution identified the right-arm co-contraction index (CCI_R), pupil dilation standard deviation, and lateral sway RMS as the top discriminating features. The 1–2 Hz EMG spectral onset peak is proposed as a spectral fidelity index for retroactive diagnosis of algorithm contamination in archival datasets. These results establish multimodal, condition-aware feature engineering as a prerequisite for valid driving style classification in driving simulators.
The heat pump air conditioning (HPAC) system constitutes a dominant energy-consuming subsystem in electric vehicles (EVs), under winter operating conditions, its energy consumption can account for up to 50% of the total energy consumption of the vehicle. As an environmentally friendly and thermos-dynamically superior alternative to R134a, R290 emerges as a promising development trend for next-generation automotive HPAC systems. This study proposes a dynamic modeling method and an intelligent control strategy based on reinforcement learning (RL) for the R290 heat pump air conditioning system. The established thermal model was validated via bench tests. The performance of R290 and R134a in the thermal model was compared, and the dynamic responses of three control algorithms: RL, proportion integration differentiation (PID), and model predictive control (MPC). The results show that the optimal filling volume of R290 is only 39% of that of R134a. R290 outperforms R134a in terms of coefficient of performance (COP) in both cooling and heating conditions, and the lower the speed, the greater the advantage. Under the 45 °C condition, the RL algorithm reduces the temperature peak by 70% compared to the PID algorithm and by 51% compared to the MPC algorithm. It also increases the COP by 3.9% and reduces energy consumption by 4% compared to PID. Furthermore, it reduces the compressor rotational speed and features a lower rotational speed change rate, which is highly beneficial for extending the compressor service life. Under the -10 °C conditions, the RL algorithm reduces overshoot by 80% and 63% compared to PID and MPC, respectively. It also increases COP by 5.8% and 3%, and reduces energy consumption by 4.7% and 1.6%.
As the carrier of the human perceptual system, the head is directly affected by vibrations transmitted from the seat. However, the biodynamic behaviour of the head-neck under different vibration conditions remains unclear. To systematically investigate the influence of backrest/headrest on the biodynamics of different head-neck regions under uniaxial ( X and Z ) and biaxial ( X + Z ) vibrations using modal and random response analyses, this study developed five 3D biodynamic models of the human-seat system, including rigid/elastic seats with/without a backrest and headrest. The results revealed three primary resonance modes in the X and Z axes for the human-seat system. Compared with the seat stiffness, the backrest/headrest lead to a greater increase in the peak frequency of the head-neck response. The backrest/headrest induced two peak values in the X -direct-axis seat-to-head transmissibility (STHT). Under uniaxial excitation, the backrest significantly reduced the X -direct-axis STHT but amplified other direct- and cross-axis STHTs. The backrest and biaxial excitation jointly affected cross-axis coupling and multimodal collaboration, significantly increasing STHT. Across all five backrest angles and three excitation conditions, peak intervertebral disc (IVD) stress consistently occurred in the C5–C6 segment. Moreover, the IVD stress response was more sensitive to X -axis excitation than to Z -axis excitation. Notably, backrest/headrest supports significantly mitigated this localized stress. In addition, spatial distribution revealed that the STHT amplitude varied significantly among different positions on the head, and was much larger than that on the neck. This study provided new research methods and a theoretical basis for the evaluation of vehicle ride comfort and the optimization of seat under complex vibrations.
High-frequency damping force fluctuations frequently occur during stroke reversal in automotive twin-tube hydraulic shock absorbers, but the underlying valve-scale fluid–structure interaction mechanism remains insufficiently understood. This study investigated the transient mechanism underlying the damping force fluctuation during the initial stage of the rebound stroke by combining shock absorber bench tests with a progressive fluid–structure interaction (FSI) framework. First, quasi-steady one-way FSI simulations under multiple operating conditions were performed to establish the baseline flow-field characteristics and shim-deformation responses of the rebound and compensation valves. Transient two-way FSI simulations were then performed over the experimentally identified fluctuation interval to resolve the sub-millisecond evolution of the local flow field and flexible-shim deformation. The results show that the rebound valve exhibits only small and stable deformation during the fluctuation interval, whereas the low-stiffness compensation shim undergoes a rapid increase in deformation from 0.21 to 0.35 mm in less than 1 ms. This rapid boundary motion causes a step-like increase in the effective flow area, a sharp decrease in local flow velocity from 1.36 to 0.015 m/s, and the formation of a localised low-pressure region. These coupled structural and hydrodynamic changes trigger the macroscopic damping force fluctuation. Based on this mechanism, a rigid limiting ring was introduced to restrict excessive motion of the compensation shim. Bench tests showed that the root-mean-square (RMS) values of the damping force fluctuations decreased by 27.3% on average and by up to 40.9% at a nominal peak piston velocity of 0.15 m/s, while the peak rebound damping force decreased by only 2.14%–7.37%. The proposed framework provides a mechanism-based approach for analysing and mitigating transient FSI-induced force instability in thin-shim hydraulic valve systems.
To address the issues of large calculation errors in the coupling loss factors (CLFs) and singular solutions in the coupling matrix during the modeling of area connections in hybrid FE-SEA models, this study proposes an input power inversion method based on power flow balance to correct the CLFs. A coupled FE-SEA area connection model for a thin-plate acoustic cavity system was established. Compared with the radiation efficiency method, the proposed approach can describe the energy coupling relationship of “excitation–structure–acoustic cavity” more directly, offering a new modeling perspective for parameter correction in hybrid FE-SEA models. Furthermore, a weighted Levenberg-Marquardt iterative algorithm is introduced to correct the ill-conditioned coupling matrices that arise in the numerical analysis of both the radiation efficiency method and the input power method, thereby further reducing the calculation errors of the CLFs. Numerical verification was conducted using three typical materials: steel plates, aluminum alloy plates, and titanium alloy plates. The results show that the values corrected using the input power method are closer to the MC reference values, with the mean relative error controlled within the range of 5%–10%. After correction by the iterative algorithm, the maximum errors of both the radiation efficiency and input power methods were reduced to within 5%, indicating that the proposed methods can effectively suppress the singular values occurring in the computation process and ensure good robustness of the CLFs at different frequencies. The MC method also verified the effectiveness of the input power method. This study is expected to provide an engineering-applicable correction approach for the parameter calibration of hybrid FE-SEA models and the NVH performance prediction of new energy vehicles.
The combined effects of rear-end configuration and underbody design on the aerodynamic performance of the “DrivAer” vehicle model are examined through computational modeling. The study makes use of available experimental data for the purpose of a comparative assessment. The numerical analysis employs the Partially-Averaged Navier–Stokes (PANS) approach, which constitutes a variable-resolution hybrid turbulence modeling framework bridging Reynolds-Averaged Navier–Stokes (RANS) and Large-Eddy Simulation (LES) methodologies. The investigation focuses on the fastback, notchback, and estate-back “DrivAer” variants, considering both detailed and simplified underbody geometry. The dynamics of the unresolved turbulence within the PANS framework is captured using a suitably modified three-equation, low–Reynolds-number RANS model founded on the eddy-viscosity hypothesis. Within this formulation, a transport equation is solved for the variable ζ u . This quantity, defined as ζ u = v u 2 ¯ / k u , corresponds to the normalized wall-normal component of the turbulence intensity and plays a central role in the model. Specifically, it accounts for the effect of Reynolds-stress anisotropy on the characteristic velocity scale employed in the formulation of turbulence viscosity. The PANS approach adopted in this study is implemented in the commercial CFD code AVL-FIRE following the formulation of Basara et al., which was used to perform all simulations. In addition to the PANS approach, all configurations under consideration are also simulated using both steady RANS and unsteady RANS (URANS) frameworks, while employing the same underlying turbulence model formulation that forms the basis of the PANS methodology. Unlike conventional RANS-based approaches, the PANS technique is capable of resolving a portion of the turbulence spectrum in accordance with the temporal and spatial resolution in relation to the underlying computational grid, while modeling only the remaining unresolved scales. As a result, PANS offers a distinct advantage in accurately representing flow unsteadiness associated with boundary-layer separation and subsequent vortex shedding from the vehicle surface, which leads to wake structures that vary significantly with rear-end shape and underbody configuration. This enhanced predictive capability is particularly evident in the computation of the aerodynamic properties of the “DrivAer” car model, where the PANS results closely reproduce experimentally observed trends.
During the start-up of opposed-piston free piston engine generator (FPEG), the linear motor operates in motor mode. The capacity of external power supply, along with the matching among motor force, initial rebound-cylinder pressure, and scavenging pressure, is critical for successful start-up of opposed-piston FPEG. In this study, a numerical model of the start-up process and a quadratic regression model based on response surface methodology were employed to predict the performance parameters of the opposed-piston FPEG during start-up, considering key controllable parameters. The applicability of the developed response surface model was evaluated using analysis of variance. Finality, based on the experimental test, the electric power demand during the start-up process was analyzed. The results indicate that, under conditions of sufficient electric power supply, motor force and initial rebound-cylinder pressure have significant effects on the start-up characteristics of opposed-piston FPEG. The start-up requirements can be satisfied when the motor force level is approximately 25% and the initial rebound-cylinder pressure is about 0.11 MPa, whereas scavenging pressure has no significant influence on the piston motion. The adjustment of scavenging pressure can focus on improving the combustion efficiency of the working medium in the cylinder, thereby reducing the number of commissioning variables during the start-up process. Furthermore, this study clarifies the electric power demand of the opposed-piston FPEG during start-up and provides guidance for the design of a matched energy storage system.
