Xiaotao Fei

Pressure losses in centralized valve-controlled hydraulic systems limit hydraulic efficiency in electric excavators. This study examined boom-cylinder extension on a 4.3 t electric excavator using a 40-point matrix of main-spool opening and motor speed. For each retained response, the steady propulsion stage (S2) was identified, and repeated-response averages formed a pressure-loss-ratio map; independent reprocessing gave a mean absolute difference of 0.40 percentage points. The ratio generally increased with motor speed at fixed opening and with decreasing opening at fixed speed. Across the 40–100% openings, the speed-induced span was 5.30–7.47 percentage points; at the nominal 15% opening, the ratio converged within 67.21–68.20%. The measured return-to-supply pressure-drop ratio increased from 0.56 to 0.60 at 40–100% to 3.41 at 15%, indicating a shift toward return-path dominance. A calibrated 16.3% simulation input represented the nominal 15% setting. Across the full map, including this calibrated correspondence, the model yielded a mean absolute relative error of 4.83% and a root mean square error of 2.54 percentage points. Simulation decomposition showed the speed-averaged combined pressure-drop share of four valve-related restrictions increasing from 26.12% at full opening to 96.50% at the calibrated small opening. The case-specific benchmark supports pump-speed/spool-opening co-calibration and future matched-boundary comparison with distributed pump-controlled systems.

The accelerating adoption of battery electric vehicles (BEVs) has heightened the need for reliable maintenance and diagnostic methods for HV electrical systems. While extensive research has focused on battery management and drive control, practical diagnostic frameworks for power-on and charging faults remain limited. This article develops a structured diagnostic methodology that bridges theoretical circuit analysis with field-oriented inspection practice. The architecture and control logic of key subsystems, including the battery pack, HV interlock loop (HVIL), power distribution unit (PDU), and AC charging circuit, are analyzed to elucidate their functional relationships. Based on these models, quantitative diagnostic procedures are proposed for common BEV malfunctions such as power-on failure, AC charging failure, and insulation faults. Tiered resistance and voltage thresholds, signal path validation, and flow-diagram-based fault isolation enable clear and reproducible inspection processes. The proposed framework not only enhances safety and repair efficiency but also establishes a foundation for intelligent, data-driven diagnostic systems in next-generation electric vehicles (EVs).

In the V-cycle of distributed electric wheel loaders (DEWLs), transport accounts for about 70% of the cycle, making energy saving urgent, while shovel-stage slip limits traction stability. This paper proposes a two-module control framework: (i) a preferred-motor transport strategy that reduces parasitic losses and concentrates operation in high-efficiency regions; and (ii) a load-ratio-based front–rear torque distribution for shoveling that allocates tractive effort according to instantaneous axle vertical loads so that each axle’s torque respects its available adhesion. For observability, we deploy a pre-calibrated lookup-table (LUT) mapping from bucket cylinder pressure to the front-axle load ratio, derived offline from a back-propagation neural network (BP-NN) fit. Tests on a newly developed DEWL show that, compared with dual-motor fixed-ratio control, transport-stage mechanical and electrical power drop by 18–37%, and drive-system efficiency rises by 6–13%. During shoveling, the strategy reduces the peak inter-axle slip from 22–35% to 13–15% and lowers the mean slip to 2.6–5.9%, suppressing sawtooth-like wheel-speed oscillations without sacrificing peak capacity. The method reduces parasitic energy flow, improves traction utilization, and is readily deployable.

Source number estimation is a key challenge in multi-sensor array signal processing, focused on accurately determining the number of signal sources based on observed data. This problem is vital for applications in radar, sonar, wireless communication, and astronomy. Despite the variety of methods devel-oped for source number estimation, a comprehensive systematic literature review (SLR) is lacking. This re-view begins by outlining traditional methods for source number estimation and assessing their performance through simulations. It then delves into the latest advancements made between 2017 and 2025, aiming to provide a thorough overview of the factors influencing source number estimation. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, seven digital databases were searched, and 23 key papers were selected after applying predefined inclusion and exclusion criteria. The results show that the performance of source number estimation algorithms is heavily impacted by varia-bles such as noise background, signal-to-noise ratio (SNR), the number of snapshots, array manifold, and array element number. Finally, this review points to future research opportunities, particularly the potential of deep learning techniques, and discusses unresolved challenges and gaps in current studies, offering rec-ommendations to steer future research in this area.

Electric wheel loaders (EWLs) have emerged as a pivotal innovation in the 2020s, representing a transformative shift toward high-efficiency, low-emission construction machinery. Despite their growing technological and environmental significance, a systematic synthesis of advancements in EWL design, energy optimization, and intelligent control remains absent in the literature. To bridge this gap, this review critically evaluates over 140 studies for comparative analysis. Building on the authors’ ongoing research, this paper categorizes EWL architectures and examines breakthroughs in hydraulic systems, drivetrain configurations, and bucket dynamics optimization. A dedicated focus is placed on energy-saving strategies, including advancements in battery technology, fast-charging infrastructure, intelligent torque distribution, and data-driven modeling of shoveling and operational resistance. The analysis reveals that integrating optimal control strategies with machine learning algorithms—such as model predictive control (MPC)—is a critical pathway to achieving energy-efficient and assisted driving in next-generation EWLs. Furthermore, this review advocates for the adoption of distributed electro-hydraulic drive systems to minimize hydraulic losses and enable efficient energy recovery during actuator control. By synthesizing these insights, this work not only highlights current technological frontiers but also proposes actionable research directions to accelerate the commercialization of intelligent, sustainable EWLs.

High-strength steel exhibits complex fracture behavior due to the interplay between shear and necking mechanisms during stamping and forming processes, posing challenges to achieving the dimensional accuracy and reliability demanded for automotive body panels. Existing prediction methods often fail to simultaneously account for both tensile and shear fracture characteristics, thereby limiting their predictive accuracy under diverse stress conditions. To address this limitation, we propose a ductile fracture criterion that integrates both tensile and shear mechanisms, calibrated using a single tensile–shear test to facilitate practical engineering applications. This study investigates the fracture characteristics of DP780 dual-phase steel through numerical analysis and tensile–shear experiments. The findings establish a relationship between stress triaxiality and ultimate fracture strain across varying stress states, represented by the B–W curve. Simulations reveal distinct stress triaxiality behaviors under different loading conditions: under uniaxial tensile loading, triaxiality ranges from 0.33 to 0.6, with fracture strain decreasing monotonically as triaxiality increases. Under shear loading, triaxiality ranges from 0 to 0.33, with fracture strain increasing monotonically as triaxiality rises. Additional bending simulations validate that this criterion, along with the B–W curve, reliably predicts the fracture behavior of DP780, offering an effective tool for predicting fracture in dual-phase steels during stamping and forming processes.

Research on electric wheel loaders (EWLs) has predominantly focused on battery management, hybrid technologies, and energy recovery. However, the influence of motor types and drivetrains on the drive performance of EWLs has received little attention in previous studies. This case study addresses this gap by examining different EWL configurations and analyzing the drive theory and force requirements by integrating classic vehicle theory with EWL-specific characteristics. The study compares an original EWL, equipped with Permanent Magnet Synchronous Motors (PMSMs) on both the front and rear axles with identical transmission ratios of 22.85, to a modified EWL, which features a Switched Reluctance Motor (SRM) on the front axle and a transmission ratio of 44.05. Walking and shoveling tests were conducted to evaluate performance. The walking test results reveal that, at motor speeds of 200 rpm, 400 rpm, and 600 rpm, energy consumption in R-drive mode is 68.56%, 71.88%, and 74.87% of that in F-drive mode when two PMSMs are used. When an SRM is applied with a transmission ratio of 44.05, these values shift to 73.90%, 70.35%, and 67.72%, respectively. This demonstrates that using the rear motor alone for driving under walking conditions can yield greater energy savings. The shoveling test results indicate that distributing torque according to wheel load reduces rear wheel slippage, and the SRM with a transmission ratio of 44.05 delivers sufficient drive force while operating within a high-efficiency speed range for the EWL.

During the operation of a wheel loader, the external load acting on the bucket undergoes many changes, resulting in significant changes in the load ratio on the front and rear axles. For this reason, controlling a standard wheel loader is not trivial. In addition, in the case of a distributed electric wheel loader (DEWL), the operating control algorithm is often complex and is, therefore, the subject of optimization studies. This study compared the electric power consumption across different vertical loads, speeds, and travel directions for single-front, single-rear, and dual-motor configurations, both during transporting and pre-shoveling operations. The analysis led to the development of control rules based on energy-saving objectives. Under the shoveling condition, it was observed that vertical loads can lead to an insufficient driving force and skidding, necessitating the proposal of a new optimized control scheme. The results revealed that the optimal solution for transporting is the single-motor drive control scheme without a mechanical connection between the front and rear motor. With the single-motor control scheme, comparing the preferred controlled motor with the unselected motor under different loads, the average electrical power savings for forward, backward, and circling were at least 3.51%, 3.12%, and 0.34%, respectively. Under the pre-shoveling condition, the optimal control scheme was identified as the single rear motor control scheme, effectively reducing electrical power consumption. In response to the issues encountered during the shoveling condition, an economical solution involving the modification of the front axle transmission ratio has been proposed, along with an optimized control scheme based on vertical load variations.

The electric wheel loader is a new prototype in powertrains and drivetrains that saves energy consumption and diminishes emissions as earthmoving machinery. Dual-motor drive in the front and rear axles of electric wheel loaders helps the distribution of drive torque. However, challenges arise during shoveling conditions, particularly when one motor generates torque exceeding the ground’s adhesion force, leading to tire slippage. This study thoroughly examines the mechanical structure of the working unit and elucidates the correlation between wheel load and hydraulic pressure in the base chamber of the tilt cylinder. This analysis is accomplished through a combination of theoretical derivations and experimental tests. The experiments involve a 5 ton rated load electric wheel loader tested across five running cases as well as weighing tests on a 15 ton rated load electric wheel loader. Based on the experiment discoveries, a dual-motor drive electric wheel loader is designed with specific transmission ratios for the front and rear drivetrains, and a torque distribution strategy is proposed based on wheel load during shoveling. Running condition tests demonstrate sufficient drive force for the new electric wheel loader, and shoveling tests reveal a significant reduction in tire slippage when employing the proposed torque distribution strategy compared to evenly distributed torque in the front and rear axles. Moreover, the driving force during the shoveling process remains undiminished. This indicates that the newly designed loader, in conjunction with the proposed strategy, exhibits excellent shoveling efficiency.

The presented research on electric wheel loaders lacks a detailed analysis of drive energy-saving during the shovel preparation phase, which is characterized by a high probability of loader tire skidding. To address this issue, this study examines the energy consumption efficiency of a two-motor distributed drive wheel loader under three drive modes including front motor drive, rear motor drive, and dual-motor drive, taking into account the change in the drive force demand caused by the bucket landing. This study finds that the motor energy conversion efficiency is the greatest in single-motor drive mode when the bucket does not generate positive pressure with the ground. In dual-motor drive mode, the total torque overcome is greater, but the motor energy conversion efficiency is the greatest when the bucket generates the greatest positive pressure with the ground. This study suggests that in future designs of electric loaders, two motors can be used to distribute the drive, but the front and rear motors should be designed to participate in the drive with a certain torque distribution ratio at different speeds and resistance to avoid the phenomenon of the bucket pressing the ground too much.