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.

Wheel loaders consume a large amount of energy, and research on energy savings in wheel loaders has been carried out for decades. This paper introduces several types of wheel loaders and compares their structures. The research progress of the energy savings of three different forms of wheel loaders is reviewed, including a diesel engine wheel loader, a hybrid wheel loader, and an electric wheel loader. In particular, the energy-saving control methods of an electric wheel loader in the working cycle are analyzed, as construction machinery electrification is an emerging trend. Based on the analysis of the driving features and the working process of a wheel loader, energy-saving control methods are introduced including the resistance reduction method, optimized control strategies, intelligent control, and unmanned WL research. Comparing various energy-saving research methods and the advantages of electric wheel loaders, the pure electric wheel loaders are advised to be researched at present and in the future. Controlling the torque distribution of the front and rear motors of electric wheel loaders and assistant drive control are proposed to be significant research prospects for energy savings in wheel loaders usage.

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.

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.

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.

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.