Search Results (2,714)

Model reference adaptive system (MRAS)-based techniques are widely used for sensorless speed estimation in permanent magnet synchronous motor (PMSM) drives. However, conventional MRAS approaches rely on PI/PID controllers in the adaptation mechanism, which require careful tuning and may suffer from performance degradation under varying operating conditions. This paper proposes a tuning-free finite speed-set model reference adaptive system (FSS-MRAS) for sensorless speed estimation in electric vehicle (EV) applications. The suggested design methodology substitutes the traditional adaptive controller design with a speed-set predictive selection approach, hence removing the need for controller parameter tuning and making the scheme more straightforward to implement. To validate the performance of the FSS-MRAS algorithm, a comprehensive EV model is simulated according to the FTP-75 driving cycle, which provides very dynamic and realistic test conditions. Simulation results prove that the suggested algorithm guarantees accurate speed estimation in all operational scenarios, including low-speed, high-speed, and high-dynamics modes. It is also demonstrated that the estimated value matches the actual value with negligible errors at the steady-state condition and stays bounded around the real value within ±40 rpm during transient dynamics conditions. Moreover, compared with the conventional MRAS scheme, the proposed approach eliminates the need for PI controller tuning while maintaining accurate and stable speed estimation under the highly dynamic operating conditions of the FTP-75 driving cycle. Additionally, the proposed scheme proves stable performance without any oscillation or divergence issues through the whole driving cycle. Full article

Accurately predicting the strain time series of orthotropic steel bridge decks (OSBDs) is highly challenging due to their strong stochasticity and nonlinear characteristics. This paper proposes a hybrid prediction framework integrating wavelet decomposition with a cascaded Convolutional Neural Network and Long Short-Term Memory architecture. Initially, the raw strain signals are decoupled into temperature-dominated low-frequency trends and vehicle-induced high-frequency dynamic components using the 6-level Daubechies 10 wavelet transform. Subsequently, a deep architecture comprising three CNN layers and two LSTM layers is constructed to precisely extract and learn the local spatial features and long-term temporal dependencies of the decoupled signals. Based on real-world monitoring data, the proposed model is comparatively evaluated against baseline models, including CNN-GRU, LSTM, and Gated Recurrent Unit (GRU), across three time horizons: 24 h, 1 h, and 10 min. The results demonstrate that the proposed method consistently exhibits superior predictive performance across multiple scales. Specifically, the mean absolute percentage error (MAPE) is strictly maintained below 0.6% across all tested horizons, with an R² reaching 0.961. Furthermore, the single-step inference latency is merely 0.63 milliseconds, which is significantly lower than conventional sensor acquisition intervals. This decouple-then-predict analytical framework effectively avoids the feature interference typically encountered when a single network directly processes complex mixed signals. Moreover, while strictly satisfying real-time computational constraints, it provides an undistorted, high-fidelity data foundation for future online fatigue evaluations and continuous state tracking of bridge structures. Full article

Despite the rapid expansion of unmanned aerial vehicle (UAV) applications across precision agriculture, logistics, infrastructure inspection, disaster response, and aerial surveying, objective and quantitative hardware evaluation criteria for UAV components remain insufficiently developed. This paper proposes quantitative key performance indicators (KPIs) for thirteen core hardware subsystems, including airframe and propulsion, battery and power supply, flight control, wireless communication, imaging (camera), Global Positioning System (GPS)/Global Navigation Satellite System (GNSS) positioning, thermal management, acoustic and vibration characteristics, AI-based autonomous flight, electromagnetic compatibility (EMC), cybersecurity, and reliability and environmental qualification, together with LiDAR payload evaluation criteria. International standardization activities by 3GPP (Release 15/17), IEEE (1936–1958 series), American society for photogrammetry and remote sensing (ASPRS), and national regulatory frameworks are synthesized to define measurable performance metrics and recommended test methods for each subsystem. An integrated KPI matrix maps application-domain-specific performance targets—encompassing surveying (real-time kinematic (RTK) horizontal accuracy ≤ 2 cm root-mean-square error (RMSE), ground sample distance (GSD) ≤ 2 cm/px), infrastructure inspection (LiDAR payload up to 8 kg, beyond visual line-of-sight (BVLOS) latency ≤ 140 ms), and logistics delivery (payload ≥ 2 kg, precision landing ≤ 50 cm)—demonstrating that no universal platform can simultaneously satisfy all domain requirements. A fuzzy-AHP weighting procedure and inter-subsystem coupling analysis are introduced to address size, weight, and power (SWaP) trade-off relationships that purely additive scoring models cannot capture. The proposed evaluation framework is intended to contribute practically to UAV standardization, certification, and quality management across the full design–procurement–operation lifecycle. Full article

Cooperative adaptive cruise control (CACC), as an extension of adaptive cruise control (ACC), is an intelligent transportation approach for connected and automated vehicles. By using vehicle-to-vehicle information, CACC improves longitudinal tracking performance, traffic throughput, and string-stable platoon behavior. However, controller tuning remains sensitive to vehicle-dynamics parameters, spacing-policy selection, fractional-order dynamics, and communication delay. This paper presents an analytical parameter-space-based fractional-order PD (FOPD) controller synthesis framework for string-stable CACC systems. For the constant-time headway spacing policy, the controller parameters are investigated in the $(k_p,k_d,\mu )$ parameter space, where the fractional differentiation order $\mu$ is considered as an additional design variable. To obtain the feasible stabilizing regions, the fractional-order characteristic equation is evaluated on the imaginary axis, and the delay-dependent stability boundaries are derived through a frequency-domain boundary-locus formulation. The stabilizing gain regions are constructed through the complex-root boundary (CRB), real-root boundary (RRB), and infinite-root boundary (IRB), which provide an interpretable graphical basis for controller-gain and fractional-order selection. In addition, the effect of the headway time on the admissible stability region is examined jointly with the fractional order. The proposed structure is implemented with a feedforward controller that uses the acceleration information of the preceding vehicle under a predecessor-vehicle-following communication topology. The selected fractional-order CACC (FO-CACC) controller is validated in an eight-vehicle platoon simulation environment and compared with integer-order ACC (IO-ACC), fractional-order ACC (FO-ACC), and integer-order CACC (IO-CACC) configurations. The results show that the proposed parameter-space approach enables systematic FOPD tuning and that the selected FO-CACC controller satisfies the frequency-domain string-stability requirement while maintaining smooth time-domain responses in position, velocity, acceleration, headway time, spacing error, and control input. Additional simulations under the New European Driving Cycle (NEDC) and the FTP-75 (Federal Test Procedure 1975) driving cycles further indicate that the proposed FO-CACC structure maintains accurate spacing regulation and bounded acceleration behavior under standard drive-cycle conditions. Overall, the results indicate that the fractional-order parameter provides an effective design freedom for improving string-stable cooperative platoon performance. Full article

This study presents the development and application of a human-centered assessment protocol for in-vehicle Human–Machine Interfaces (HMIs), suitable for real-vehicle evaluation and designed to support future implementation in simulated environments. The objective is to define a structured protocol to assess user experience (UX) in physical testing environments and simulated virtual scenarios. The protocol evaluates user performance and subjective perceptions while interacting with key interface components, including the instrument cluster, central display, steering-wheel controls, and ergonomic adjustment commands, establishing a methodological baseline for future digital and simulation-based evaluations. The work describes the process from requirement analysis to protocol design and user testing, conducted in collaboration with an automotive manufacturer. Participants performed predefined interaction tasks reflecting typical phases of vehicle use. Objective metrics, including task completion times and error rates, were collected alongside subjective evaluations of perceived usability, perceived quality, and satisfaction. Data were analyzed through t-tests and ANOVAs to investigate differences across user groups and interface elements. Beyond the empirical findings, the main contribution of this work lies in delivering a reusable human-centered evaluation framework supporting both current in-vehicle assessments and future simulated environments through automated interaction logging, behavioral tracking, and multimodal data capture while preserving methodological continuity and human-centered validity. Full article

Lithium-ion batteries (LIBs) have been widely used in different fields as energy storage systems, such as electric vehicles and power grids. The performance of LIBs degrades with usage, which poses challenges for battery management. Thus, accurate online estimation of the state of health (SOH) is critical to ensure reliability and prolong the service time of LIBs. To achieve this, data-driven methods have become popular due to the capability of learning the mapping between SOH and measurements without prior knowledge of aging mechanisms. However, the online estimation performance of these methods cannot be guaranteed, since the models are trained offline and do not have the capability of online updating when new data are collected. In addition, the inputs for these methods are constructed with the voltage–capacity (V-Q) curve within a fixed voltage interval, which can hardly be realized in real-life applications due to the randomness of the charging or discharging process. This study proposes a Sequential Gaussian Process (Seq-GP) model-based LIB SOH estimation method, where model parameters can be updated using newly collected LIB data, such that online estimation can be fulfilled. Moreover, a novel feature extraction method is presented using random parts of the LIB V-Q curve to meet the requirements for practical applications. The proposed method is evaluated on two public battery datasets, showing competitive estimation accuracy together with online updating, uncertainty quantification, and low computational cost under the tested protocol. The results will be beneficial for online SOH estimation of LIBs in practical scenarios. Full article

Unmanned Aerial Vehicles (UAVs) have become critical platforms for missions such as surveillance, reconnaissance, and target tracking, which require real-time decision-making, reliable sensing, and efficient resource utilization. However, limited onboard computing capacity, energy constraints, variable terrain conditions, and situations where targets are partially or fully obscured limit the performance of traditional fixed-configuration sensing and tracking approaches. In this study, we propose a context-aware and adaptive UAV-based target detection and tracking framework that dynamically selects the most appropriate detection and tracking algorithm by jointly evaluating terrain characteristics and mission requirements. The proposed system includes a three-stage terrain analysis module supported by HSV color space filtering, Canny edge detection, Laplacian texture variance, and contrast-based features. In cases where color-based classification is insufficient, Random Forest-based classification is used to distinguish between vegetation, bare ground, and urban areas; the terrain classification model achieves approximately 90% accuracy during the training and testing process. In the target detection phase, a YOLOv11-based model was trained on a specialized tank dataset created from various sources and labeled in YOLO format, achieving an mAP50 performance of approximately 85%. In the tracking phase, single-object and multi-object tracking algorithms are selected via a scoring-based decision mechanism depending on the terrain type and mission scenario. Additionally, a hybrid anomaly detection mechanism that evaluates target loss, sudden bounding box changes, and view inconsistencies was integrated into the system, thereby enhancing tracking reliability and enabling the re-detection or algorithm switching process when necessary. Experimental results demonstrate that the proposed context-aware approach can reduce computational load while maintaining tracking robustness under various environmental conditions. These findings highlight that environmental awareness and adaptive algorithm selection can make significant contributions to autonomy, operational efficiency, and real-time reliability in UAV-based imaging systems. Full article

Accurate junction temperature (T_j) sensing is essential for the reliability of silicon carbide (SiC) power modules in electric vehicles. Nonetheless, the physical separation and consequent thermal signal delay between sensing elements and chips pose significant challenges to precise junction temperature monitoring. To solve this issue, an embedded temperature sensing structure integrated into the designed double-sided cooling (DSC) SiC power module is proposed, which leverages 3D vertical interconnects to enhance temperature observability. The customized design of a copper spacer serves as the primary heat dissipation path and electrical connection between the upper and lower chips in the same location. A compact thermal resistance network and 3D finite-element simulations are developed to reveal the vertical thermal coupling between the spacers and the chips, enabling accurate junction temperature estimation from spacer temperature. The proposed concept is experimentally validated on a fabricated prototype using embedded K-type thermocouples and an IR camera under power cycling conditions. The measured temperature differences between the copper spacers and the junction temperature are maintained within approximately 0.5 –2 °C under the tested operating range. This approach provides a potential application in real-time condition monitoring and thermal management in high-power-density electric drives. Full article

With the rapid development of intelligent connected vehicles and autonomous driving, real-time and accurate road condition perception has become increasingly critical. Aiming at the limitations of traditional direct and indirect detection methods, this paper proposes an ultrasonic-based real-time detection system for road roughness. Most urban roads today feature asphalt pavements; therefore, this system focuses its research on asphalt pavements. Under the same pavement type (asphalt roads), there is a strong correlation between pavement roughness and the friction coefficient. By measuring the roughness of different pavements, the friction coefficient is estimated using the fuzzy processing method. Then the system through measuring ultrasonic echo amplitude and sensor–road distance, combined with software digital filtering, dual-parameter compensation (distance and temperature–humidity), probabilistic statistical analysis, and fuzzy inference, the mapping relationship among echo signals, road roughness and friction coefficient is established. The system mainly includes an ultrasonic transceiver module, a hardware signal conditioning module, and an MCU-based data processing, display and transmission module. Both simulated experiments and real asphalt pavement tests are carried out for verification. The results show that the system can effectively suppress noise, compensate distance attenuation and environmental interference, and achieve accurate real-time detection of road roughness, with a relative error less than 10% compared with the reference value. The proposed system can provide reliable data support for vehicle active safety systems and autonomous driving applications. Full article

To address unavailable global navigation satellite system (GNSS) signals, dense metallic equipment, valve-tower occlusion, and the insufficient robustness of single-sensor localization in unmanned aerial vehicle (UAV) inspection of converter valve halls, this paper proposes a non-line-of-sight (NLOS)-aware LiDAR-ultra-wideband (UWB) fusion localization method. The method uses LiDAR odometry to provide continuous local motion constraints and UWB ranging to provide global distance constraints. The geometric relationship among the UAV, UWB anchors, and valve-hall obstacles is used to evaluate the NLOS risk of each UWB link, and the equivalent ranging variance is adaptively adjusted before tight fusion optimization. To avoid overextending simulation conclusions, this study focuses on localization-layer modeling and simulation-based validation rather than full energized valve-hall flight deployment. In the grouped-bushing valve-hall scenario, the proposed method achieves an RMSE of 0.30 m, a mean error of 0.29 m, a P95 error of 0.43 m, and a maximum error of 0.48 m, reducing the RMSE by 50.0% compared with ordinary tight LiDAR-UWB fusion. Additional Monte Carlo tests under different trajectories, anchor layouts, anchor installation errors, and obstacle densities further verify the robustness of the proposed weighting mechanism. The results indicate that the method can suppress LiDAR accumulated drift and reduce the influence of UWB NLOS ranging in GNSS-denied metallic indoor environments, while real converter-valve-hall flight tests under energized electromagnetic conditions remain necessary before engineering deployment. Full article

Continuous full-domain vehicle trajectories are essential for smart highway monitoring, but single-sensor roadside perception is limited by physical coverage, occlusion, and environmental sensitivity. To address continuous trajectory tracking across multiple roadside-sensing domains, this study proposes a real-time, full-domain highway trajectory tracking framework based on radar–camera fusion, improved Deep-SORT, and inverse covariance intersection. At the local perception level, a two-stage object-level and decision-level fusion model is constructed, and Deep-SORT is improved using a CIoU matching strategy and an occluded target tracking controller to enhance local multi-object tracking continuity. At the cross-domain association level, a geometry-motion consistency stepwise calibration method is developed to unify adjacent sensing domains, and a CATS-ICI trajectory stitching strategy is introduced to improve trajectory association and state smoothness during sensor handover. The proposed framework was validated on a real highway test section with roadside radar, video, and drone-based ground-truth trajectories. Experimental results show that the full local method achieves an EMOTA of 92.35%, and the reconstructed full-domain trajectories achieve a successful trajectory matching rate of 98.4% under the 452 vehicles/10 min test condition. Additional ablation experiments further verify the contributions of radar–camera fusion, CIoU, OTTC, GMCSC, CATS, and ICI. These results demonstrate that the proposed framework can provide continuous and reliable full-domain vehicle trajectories for real-world highway monitoring. Full article

Advanced biofuels represent a key option for transport decarbonization, particularly in sectors where electrification is constrained by technical and economic barriers. Their compatibility with existing vehicle fleets and fuel distribution infrastructure enables rapid deployment without the need for major capital investments. In local public transport, biodiesel (FAME), hydrotreated vegetable oil (HVO), and biomethane are mature solutions capable of delivering greenhouse gas emission reductions of 60–90% compared with fossil fuels. Among these, HVO is particularly promising, as an extensive body of literature has consistently shown its potential to significantly reduce engine-out emissions, especially particulate matter (PM) and nitrogen oxides (NO_x). This study reports the results of an experimental campaign carried out on a diesel-powered local public transport bus equipped with a Euro III engine and lacking particulate matter and NO_x after-treatment systems. Emissions were measured using a portable emissions measurement system (PEMS) under real driving conditions, operating the vehicle with neat diesel, a 15% HVO blend, and a 70% HVO blend. Tests were conducted over urban and extra-urban routes. The results show that NO_x emissions decrease proportionally with increasing HVO content, with high-blend ratios (HVO70) yielding estimated reductions of approximately 13–18%, and up to 23% under carefully controlled and comparable urban driving conditions. Based on these findings and the existing literature, HVO proves to be a useful instrument to meet 2025–2030 climate and air quality targets (particularly NO_x and PM emission reductions), alongside electrification and modal shift measures, if used in public transport fleets. Full article

This paper introduces a multi-feature coordinated adaptive equivalent consumption minimization strategy (MFCA-ECMS) using fuzzy logic control (FLC) to enhance hydrogen efficiency in fuel cell hybrid electric commercial vehicles (FCHECVs) and extend the lifespan of the fuel cell system (FCS), contributing to sustainable, low-carbon transport. First, a baseline ECMS model is established for the FCHECV, whilst the optimal equivalent factor (EF) is determined using a multi-island genetic algorithm (MIGA) based on representative driving cycles. Second, an adaptive EF framework is developed to overcome the inherent limitation of conventional ECMS—its reliance on a fixed EF—by dynamically integrating three operational features: variation in the battery’s state of charge (SOC), the rate of change in the FCS’s output power, and fluctuations in vehicle power demand. Third, feature-specific adaptive weights are assigned and updated in real time using a fuzzy inference system to regulate the EF online, incorporating multiple features. Simulations are conducted under different initial SOC levels ($90\%$ and $45\%$) across different driving cycles. The results demonstrate that the MFCA-ECMS consistently reduces hydrogen consumption (HC). Compared to the charge-depleting and charge-sustaining (CD-CS) strategy, it achieves HC reductions of $17.98\%$ on the stochastic driving cycle (Random-C) and $18.73\%$ on the urban dynamometer driving schedule (UDDS), outperforming both CD-CS and conventional ECMS in all tested scenarios. Furthermore, the MFCA-ECMS actively suppresses FCS power fluctuations. Regardless of the initial SOC, the proportion of power change rates within the reasonable range exceeds $97\%$, thereby contributing to extending the FCS lifespan. This reduces emissions and operating costs, enabling sustainable hydrogen-powered commercial vehicle deployment. Full article

To address the limitations of existing low-temperature evaluation methods for battery electric vehicles (BEVs) in terms of real-world road adaptability, test consistency, and multidimensional performance assessment, this study proposes a standardized on-road testing and multidimensional adaptability evaluation system for BEVs in frigid environments. To address the issues that conventional bench tests cannot adequately replicate real-world road environments, routine road tests lack consistency, and existing evaluation indicators pay insufficient attention to charging efficiency and cabin heating performance, this study defines the ambient temperature for road testing, low-speed steady-state driving conditions, and the conditions for ensuring consistency in road testing. It also establishes a cold-environment adaptability evaluation system comprising three dimensions—driving range, charging efficiency, and heating, ventilation, and air conditioning (HVAC) heating performance—and four evaluation indicators: the driving range degradation rate in cold environments, charging time per 100 km, HVAC heating duration, and HVAC heating energy consumption per unit cabin volume. Field tests were conducted on 10 representative BEVs in real-world road conditions near −20 °C in Heihe City, Heilongjiang Province, China. The results indicate that the average range degradation rate for these 10 models in cold environments was 60.7%, and approximately 60% of the vehicles could complete a 100 km charge in under 30 min; the average HVAC heating time was 34 min, with an average power consumption of 9.2 kWh. The tests also revealed that the heating efficiency and thermal comfort of single-heat-pump HVAC systems at −20 °C still have room for improvement, and that the uniformity of cabin temperature distribution and consistency in foot temperature between the left and right sides significantly affect thermal comfort. The evaluation method proposed in this study can serve as a reference for testing the cold-weather adaptability of BEVs, as well as for optimizing thermal management systems and developing vehicle performance. Full article

Battery use in trolleybuses improves energy efficiency and enables driving outside routes with overhead contact lines. This paper analyses the ageing process of lithium-ion batteries by determining the State of Heath (SOH) curve based on real-world data collected during six and a half years of trolleybus operation. The battery, manufactured using NMC technology (lithium-nickel-manganese-cobalt LiNiMnCoO₂), was used as an onboard energy storage unit. The battery pack powers the trolleybus on the non-electrified route segments and improves its energy efficiency. In this paper, the ageing process of the lithium-ion battery in such a vehicle was investigated, using recorded data for each day of operation between 2016 and 2023. The SOH of the battery was estimated on the basis of three criteria: specific SOC range, specific battery voltage range and specific battery pack temperature range. Under these circumstances, the values of electric charge and energy flow into the battery were analysed, allowing the obtainment of the battery SOH value. Empirical distributions of random variables related to the minimum and maximum battery temperature and battery discharge current were presented. Based on these empirical distributions, statistical descriptors representing health indicators were calculated. The environmental factors (temperature, SOC, discharge and charge currents) that had a significant impact on the ageing process of the battery under test were analysed as well. Full article