Search Results (335)

This paper presents an innovative approach to optimize real-time energy management in fuel cell electric vehicles (FCEVs) through an integrated EMS (iEMS) framework based on a nested concept. Central to our method are two LSTM-based speed prediction models, trained and validated on open-source datasets to enhance adaptability and efficiency. The first model, trained on a 27 h real-time database, is embedded within the iEMS for dynamic real-time operation. The second model assesses the impact of incorporating external traffic data on the prediction accuracy, offering a systematic approach to refining speed prediction models. The results demonstrate significant improvements in fuel efficiency and overall performance compared to existing models. This study highlights the promise of data-driven AI models in next-generation FCEV energy management, contributing to smarter and more sustainable mobility solutions. Full article

This article presents the results of an experimental study focused on evaluating the potential to harvest electrical energy from vertical vibrations affecting a child car seat installed on an ISOFIX base with a support leg during real driving conditions. The objective was to measure vibration levels in the seat structure and assess the feasibility of converting this mechanical energy into electrical power. The study involved two child seat models, each tested under loads of 9 kg and 15 kg, while driving over smooth asphalt, damaged asphalt, and speed bumps. Acceleration data were collected at three key structural locations: the seat surface, the ISOFIX base, and the support leg. These measurements served as the basis for estimating the mechanical energy available and the resulting electrical output. Findings show that in poor road conditions, the system can generate enough energy to power a 10 µW sensor for more than 42 days. The results confirm the feasibility of using vibration energy harvesting to supply smart safety features such as presence detection, temperature monitoring, or posture sensing in child seats, without the need for batteries or a connection to the vehicle’s electrical system. Full article

This paper presents a new strategy for wind energy harvesting to enhance the performance of the electric-wind vehicle (EWV). A wind turbine is mounted on the front of an electric-wind vehicle model to capture wind that blows in the opposite direction of the moving vehicle. When the primary battery runs low, the generator switches on, converting wind energy into electricity and storing it in a backup battery. The switching circuit is developed to alternate between the main battery and the backup battery based on the battery capacity level. During the movement of the EWV, the backup battery charges using the wind turbine while the main battery discharges. A modified sliding mode control is used to track the reference speed of the EWV and to regulate the speed by switching between the two batteries. Several scenarios are applied to investigate the proposed strategy. The results show that the proposed strategy can save power by 30% compared to the conventional strategy. Moreover, the modified sliding mode control enhances the EWV’s dynamic performance in contrast to PID control, which shows poor performance (low rise time and low overshoot). Full article

Energy management for hybrid electric commercial vehicles, involving continuous power output and discrete gear shifting, constitutes a typical mixed-integer programming (MIP) problem, presenting significant challenges for real-time performance and computational efficiency. To address this, this paper proposes a physics-informed neural network-optimized model predictive control (PINN-MPC) strategy. On one hand, this strategy simultaneously optimizes continuous and discrete states within the MPC framework to achieve the integrated objectives of minimizing fuel consumption, tracking speed, and managing battery state-of-charge (SOC). On the other hand, to overcome the prohibitively long solving time of the MIP-MPC, a physics-informed neural network (PINN) optimizer is designed. This optimizer employs the soft-argmax function to handle discrete gear variables and embeds system dynamics constraints using an augmented Lagrangian approach. Validated via hardware-in-the-loop (HIL) testing under two distinct real-world driving cycles, the results demonstrate that, compared to the open-source solver BONMIN, PINN-MPC significantly reduces computation time—dramatically decreasing the average solving time from approximately 10 s to about 5 ms—without sacrificing the combined vehicle dynamic and economic performance. Full article

This study develops a novel electric-driven metering device to address the mismatch between the seeder rotation speed and vehicle speed in traditional mechanical precision hill direct-seeding metering devices for rice, which is caused by wheel slippage. The device integrates a Global Navigation Satellite System (GNSS) speed measurement module and an optimised incremental Proportional-Integral-Derivative (PID) control algorithm, enabling precise seeding through electric drive. A multidisciplinary collaborative design approach is employed, and field experiments are conducted to evaluate the performance of the novel device under conditions of vehicle speeds ranging from 3 to 5 km/h, theoretical hill spacings of 0.15–0.25 m, and seeding rate adjustment positions of 1/3–1. The experiments use two rice varieties, “Longken 2021” from the northern rice growing region and “Jingliangyou Huazhan” from the southern rice growing region. The results demonstrate that the novel electric-driven metering device significantly outperformed the traditional mechanical device in terms of seeding precision, hill formation performance, and seeding rate accuracy. The novel device achieves a qualified rate of seeds per hill of 90.54%, with seedling omission and seed damage rates reduced to 4.98% and 0.69%, respectively. The hill diameter qualification rate increases to 95.21%, with no empty hills observed. The coefficient of variation of seeds per hill is maintained at 21.98%, meeting the agronomic requirement of 2–12 seeds per hill for conventional rice. However, for seeds with high moisture content and poor flowability (soaked seeds), the seed damage rate increases slightly by 0.47 percentage points. This study provided an efficient and reliable technical solution for the intelligent upgrading of precision rice direct-seeding equipment. Full article

This paper presents a novel methodology for minimizing power losses in brushless DC (BLDC) motors through the implementation of adaptive flux control techniques. Conventional motor control strategies, such as direct torque control (DTC), typically employ fixed flux values, which often result in suboptimal performance, particularly under dynamic load and speed variations. To mitigate this inherent limitation, two adaptive flux control methods are introduced: incremental conductance (IncCond) and fuzzy logic. These proposed strategies facilitate real-time dynamic adjustment of the stator flux, thereby optimizing motor performance and significantly enhancing system efficiency. Experimental validation confirms the efficacy of these adaptive techniques, demonstrating substantial improvements in power loss reduction and overall efficiency when compared to traditional fixed flux control strategies. Notably, the fuzzy logic control strategy achieves the highest efficiency, registering a system efficiency of $66.59$%, which surpasses both the incremental conductance method and conventional fixed flux control. These findings underscore the considerable potential of adaptive flux control in applications where energy efficiency is paramount, including electric vehicles and renewable energy-driven systems. Full article

Under a 2021 amendment to German law, the KelRide project became the first public on-demand service operating electric autonomous vehicles (AVs) without fixed routes on public roads. This paper addresses two notable gaps in the literature by (1) conducting an ex post evaluation of demand predictions for a non-infrastructure (Mobility-on-Demand (MoD)) project and (2) using real-world data to analyze how demand responds to key Autonomous Mobility-on-Demand (AMoD) system parameters in a rural context. Earlier simulation-based demand forecasts are compared to observed booking data, and the recalibrated model is used to investigate the sensitivity of passenger numbers to vehicle speed, fleet size, service area, operating hours, and idle vehicle positioning. Results show that increasing vehicle speed leads to a superlinear rise in passenger numbers—especially at small fleet sizes—while demand saturates at large fleet sizes. A linear increase in demand is observed with expanding service areas, provided fleet size is sufficient. Extending operating hours from 9 a.m.–4 p.m. to full-day service increases demand by a factor of two to four. Passengers numbers also vary notably depending on the positioning of idle vehicles. Consistent with empirical findings, the analysis underscores that raising AV speed is essential for ensuring the long-term viability of autonomous mobility services. Full article

The energy generated from renewable sources has an intermittent nature since solar irradiation and wind speed vary continuously. Hence, their energy should be stored to be utilized throughout their shortage. There are various forms of energy storage systems while the most widespread technique is the battery storage system since its cost is low compared to other techniques. Therefore, batteries are employed in several applications like power systems, electric vehicles, and smart grids. Due to the merits of the lithium-ion (Li-ion) battery, it is preferred over other kinds of batteries. However, the accuracy of the Li-ion battery model is essential for estimating the state of charge (SOC). Additionally, it is essential for consistent simulation and operation throughout various loading and charging conditions. Consequently, the determination of real battery model parameters is vital. An innovative application of the red-billed blue magpie optimizer (RBMO) for determining the model parameters and the SOC of the Li-ion battery is presented in this article. The Shepherd model parameters are determined using the suggested optimization algorithm. The RBMO-based modeling approach offers excellent execution in determining the parameters of the battery model. The suggested approach is compared to other programmed algorithms, namely dandelion optimizer, spider wasp optimizer, barnacles mating optimizer, and interior search algorithm. Moreover, the suggested RBMO is statistically evaluated using Kruskal–Wallis, ANOVA tables, Friedman rank, and Wilcoxon rank tests. Additionally, the Li-ion battery model estimated via the RBMO is validated under variable loading conditions. The fetched results revealed that the suggested approach achieved the least errors between the measured and estimated voltages compared to other approaches in two studied cases with values of 1.4951 × 10⁻⁴ and 2.66176 × 10⁻⁴. Full article

With the continuous development of high-speed electric multiple units (EMUs), vibration issues of vehicles have become increasingly prominent. During operation, the underfloor equipment installed on the carbody is subjected to random multi-point vibrations transmitted from the carbody, inducing significant fatigue damage. This paper presents a comprehensive analysis of multi-channel vibration environment data for various underfloor equipment across different operating speeds obtained through on-site measurements. A spectral synthetic method grounded in statistical principles is then proposed to generate vibration environment spectra for diverse underfloor equipment. Finally, utilizing fatigue analysis in the frequency domain, the fatigue damage to underfloor equipment is assessed under different operational environments. The research results show that the vibration environment spectrum of the underfloor equipment in high-speed EMU trains differs significantly from the vibration spectrum specified in the IEC 61373 standard, especially at high frequencies. Despite this difference in spectral characteristics, the overall vibration energy values of the two spectra are comparable. Additionally, the vibration spectra of different underfloor equipment exhibit variations that can be attributed to their installation positions. As operational speed increases, the fatigue damage to the underfloor equipment exhibits exponential growth. However, the total accumulated fatigue damage remains relatively low, consistently staying below a value of 1. Full article

In recent years, the rapid spread of electric vehicles (EVs) has intensified the competition to develop power units for EVs. In particular, improving the driving range of EVs has become a major topic, and in order to achieve this, many studies have been conducted on improving the efficiency of EV power units. In this study, we propose a multiple-inverter-drive permanent magnet synchronous motor based on an 8-pole, 48-slot structure, which is commonly used as an EV motor. The proposed motor is composed of two completely independent parallel inverters and windings, and intermittent operation is possible; that is, only one inverter and one parallel winding is used depending on the situation. In the proposed motor, we compare losses including stator iron loss, rotor iron loss, and magnet eddy current loss by PWM voltage inputs for some stator winding topologies, we show that the one-side winding arrangement is the most efficient during intermittent operation, and that it is more efficient than normal operation especially in the low-speed, low-torque range. Finally, through a vehicle-driving simulation considering the efficiency map including motor loss and inverter loss, we show that the intentional use of intermittent operation can improve electrical energy consumption. Full article

Few studies have examined Hybrid Aquatic–Aerial Vehicles (HAAVs), autonomous vehicles designed to operate in both air and water, especially those that are aircraft-launched and recovered, with a variable-sweep design to free dive into a body of water and breach under buoyant and propulsive force to re-achieve flight. The novel design research examines the viability of a recoverable sonar-search child aircraft for maritime patrol, one which can overcome the prohibitive sea state limitations of all current HAAV designs in the research literature. This paper reports on the analysis from computational fluid dynamic (CFD) simulations of such an HAAV diving into static seawater at low speeds due to the reverse thrust of two retractable electric-ducted fans (EDFs) and its subsequent breach back into flight initially using a fast buoyancy engine developed for deep-sea research vessels. The HAAV model entered the water column at speeds around 10 ms⁻¹ and exited at 5 ms⁻¹ under various buoyancy cases, normal to the surface. Results revealed that impact force magnitudes varied with entry speed and were more acute according to vehicle mass, while a sufficient portion of the fuselage was able to clear typical wave heights during its breach for its EDF propulsors and wings to protract unhindered. Examining the medium transition dynamics of such a novel HAAV has provided insight into the structural, propulsive, buoyancy, and control requirements for future conceptual design iterations. Research is now focused on validating these unperturbed CFD dive and breach cases with pool experiments before then parametrically and numerically examining the effects of realistic ocean sea states. Full article

This study introduces a Multiscale Dual-Attention U-Net (TS-MSDA U-Net) model for long-term time series synthesis. By integrating multiscale temporal feature extraction and dual-attention mechanisms into the U-Net backbone, the model captures complex temporal dependencies more effectively. The model was evaluated in two distinct applications. In the first, using multivariate datasets from 70 real-world electric vehicle (EV) trips, TS-MSDA U-Net achieved a mean absolute error below 1% across key parameters, including battery state of charge, voltage, acceleration, and torque—representing a two-fold improvement over the baseline TS-p2pGAN. While dual-attention modules provided only modest gains over the basic U-Net, the multiscale design enhanced overall performance. In the second application, the model was used to reconstruct high-resolution signals from low-speed analog-to-digital converter data in a prototype resonant CLLC half-bridge converter. TS-MSDA U-Net successfully learned nonlinear mappings and improved signal resolution by a factor of 36, outperforming the basic U-Net, which failed to recover essential waveform details. These results underscore the effectiveness of transformer-inspired U-Net architectures for high-fidelity multivariate time series modeling in both EV analytics and power electronics. Full article

Enhanced Battery Management Systems (BMS) are essential for improving operational efficacy and safety within Electric Vehicles (EVs), especially in tropical climates where traditional systems encounter considerable performance constraints. This research introduces a novel two-tiered deep learning framework that utilizes a two-stage Long Short-Term Memory (LSTM) framework for precise prediction of battery voltage and SoC. The first tier employs LSTM-1 forecasts individual cell voltages across a full-scale 120-cell Lithium Iron Phosphate (LFP) battery pack using multivariate time-series data, including voltage history, vehicle speed, current, temperature, and load metrics, derived from dynamometer testing. Experiments simulate real-world urban driving, with speeds from 6 km/h to 40 km/h and load variations of 0, 10, and 20%. The second tier uses LSTM-2 for SoC estimation, designed to handle temperature-dependent voltage fluctuations in high-temperature environments. This cascade design allows the system to capture complex temporal and inter-cell dependencies, making it especially effective under high-temperature and variable-load environments. Empirical validation demonstrates a 15% improvement in SoC estimation accuracy over traditional methods under real-world driving conditions. This study marks the first deep learning-based BMS optimization validated in tropical climates, setting a new benchmark for EV battery management in similar regions. The framework’s performance enhances EV reliability, supporting the growing electric mobility sector. Full article

This paper focuses on the analysis of a new modulation method based on the reverse conduction of metal–oxide–semiconductor field-effect transistors (MOSFETs) for a three-phase voltage-feed full-bridge converter with two-switched transistors. The implementation of the proposed method allows efficient converter performance during regenerative braking of a brushless DC machine. It does not require any additional components such as power switches, sensors, and high-performance microcontrollers. Previously known classical modulation methods were characterised by significantly lower efficiency of the converter due to diode conduction. The operating principle of the modified modulation method is clearly explained in detail with mathematical and simulation analyses presented. The theoretical results obtained were verified experimentally, demonstrating that the maximum efficiency of the converter increased from 88% (for classical modulation) to 95% with the new modulation strategy. The developed solution is dedicated to electric vehicles and enables effective regenerative braking even at low speeds. Full article

Dog clutches are superior to synchromesh units due to much less wear caused by friction but require an external torque source to synchronize the rotation speeds. The current trend in e-mobility to use the driving motor of an electric vehicle as this source just creates another problem, which is known as torque holes. In this work, we propose a novel double-sided dog clutch that synchronizes the speeds electromagnetically by itself avoiding mechanical contact between the parts. A shift sleeve, two coils placed coaxially in their stators, and two complementary rings form an electromagnetic reluctance actuator, which is integrated inside the gearbox between two gearwheels and represents the double-sided clutch. Thus, intermediate parts between the shift sleeve and the actuator are not required. Both actuator sides can produce axial force and electromagnetic torque. However, torques and forces are generated simultaneously on both sides. Therefore, a special control algorithm is developed to keep the resulting axial force approximately equal to zero while the torque is generated in the neutral gear position. After the synchronization, the axial force is applied on the corresponding side to shift the required gear engaging the shift sleeve teeth directly with the slots of the complementary ring mounted on the gearwheel. So, an axial contact of the teeth at an unaligned state, which can lead to unsuccessful shifting, is avoided. A testrig, which includes a clutch prototype and a testing two-speed gearbox, has been designed and built. The developed theoretical ideas have been verified during the experiments under different conditions. The experiments confirm that the actuator can reduce positive and negative speed differences on both sides and subsequently shift the gear without a shift sleeve collision at misaligned angular positions. Full article