Hybrid Electric Vehicles (HEVs) have been proven to be a promising solution to environmental pollution and fuel savings. The benefit of the solution is generally realized as the amount of fuel consumption saved, which by itself represents a challenge to develop the right energy management strategies (EMSs) for HEVs. Moreover, meeting the design requirements are essential for optimal power distribution at the price of conflicting objectives. To this end, a significant number of EMSs have been proposed in the literature, which require a categorization method to better classify the design and control contributions, with an emphasis on fuel economy, providing power demand, and real-time applicability. The presented review targets two main headlines: (a) offline EMSs wherein global optimization-based EMSs and rule-based EMSs are presented; and (b) online EMSs, under which instantaneous optimization-based EMSs, predictive EMSs, and learning-based EMSs are put forward. Numerous methods are introduced, given the main focus on the presented scheme, and the basic principle of each approach is elaborated and compared along with its advantages and disadvantages in all aspects. In this sequel, a comprehensive literature review is provided. Finally, research gaps requiring more attention are identified and future important trends are discussed from different perspectives. The main contributions of this work are twofold. Firstly, state-of-the-art methods are introduced under a unified framework for the first time, with an extensive overview of existing EMSs for HEVs. Secondly, this paper aims to guide researchers and scholars to better choose the right EMS method to fill in the gaps for the development of future-generation HEVs.

In order to detect the incipient fault of rolling bearings and to effectively identify fault characteristics, based on amplitude-aware permutation entropy (AAPE), an enhanced method named hierarchical amplitude-aware permutation entropy (HAAPE) is proposed in this paper to solve complex time series in a new dynamic change analysis. Firstly, hierarchical analysis and AAPE are combined to excavate multilevel fault information, both low-frequency and high-frequency components of the abnormal bearing vibration signal. Secondly, from the experimental analysis, it is found that HAAPE is sensitive to the early failure of rolling bearings, which makes it suitable to evaluate the performance degradation of a bearing in its run-to-failure life cycle. Finally, a fault feature selection strategy based on HAAPE is put forward to select the bearing fault characteristics after the application of the least common multiple in singular value decomposition (LCM-SVD) method to the fault vibration signal. Moreover, several other entropy-based methods are also introduced for a comparative analysis of the experimental data, and the results demonstrate that HAAPE can extract fault features more effectively and with a higher accuracy.

Increasing environmental issues and energy crises led to rapid developments of hybrid electric vehicles, especially the planetary hybrid powertrain system (PHPS). This paper presents a comprehensive review of the PHPS, focusing primarily on contributions in the aspect of configuration, classification and comparison. In this work, a new classification method for PHPS architectures is proposed according to the number of electric motors (EMs). In addition, two kinds of PHPS, in the new classification framework, are extensively emphasized in terms of its architectures, advantages and disadvantages. Furthermore, the port diagrams of representative architectures are presented to provide an intuitive method for power flow representation. Finally, a conclusion is made to provide an insight for developing PHPS as well.

The tribological performance of the friction pair between the rotor and finger feet is a crucial index affecting the service life of finger seals. In recent years, the surface texture has attracted a considerable number of researchers owing to its extraordinary potential in improving antifriction and wear resistance. This paper, inspired by snakeskins, introduces three texture forms (e.g., diamond, ellipse, and hexagon) into the rotor. The effects on finger-sealing performance are analyzed by considering finger seals’ varied working conditions. First, a numerical model of textured finger seals under hydrodynamic lubrication is established based on the Reynolds equation. Then, the sealing performance analysis of textured finger seals is performed considering varied working conditions given rotation speed, pressure difference, seal clearance, and working temperature. The numerical results show that: (1) the textured domain produces a noticeable hydrodynamic pressure effect and cavitation, which effectively improves the bearing capacity of the fluid film; (2) the higher the rotation speed or the lower the inlet/outlet pressure difference, the stronger the dynamic pressure effect of textured finger seals and the better the antifriction and wear resistance; (3) for good antifriction and wear resistance of a textured finger seal, the seal clearance should be as shallow as possible (≤10 μm), and the working temperature should be as low as possible (≤120 °C); and (4) the ellipse texture has a higher average dimensionless pressure and a lower friction coefficient, which is superior to diamond and hexagon ones in terms of friction and wear performance.

In order to solve the problems of low accuracy and low efficiency of point cloud registration for stereo camera systems, we propose a binocular stereo camera point cloud registration method based on IWOA and Improved ICP. We propose the following approaches in this paper—the registration process is divided into two steps to complete the initial coarse registration and the exact registration. In the initial registration stage, an improved Whale Optimization Algorithm (IWOA) based on nonlinear convergence factor and adaptive weight coefficients was proposed to realize the initial registration in combination with the RANSAC algorithm, and the obtained transformation matrix was used as the initial estimate of the subsequent exact registration algorithm. In the second step of the exact registration stage, an IICP algorithm with the introduction of normal vector weighting constraints at key points was proposed for achieving point cloud exact registration. This algorithm was verified by using Stanford point clouds (bunnies and monkeys) and our own point clouds algorithm, and the proposed algorithm in this paper has high registration accuracy, improved registration speed, and convergence speed.

This article presents an energy management method for a parallel hybrid electric vehicle (HEV) based on approximate Pontryagin’s Minimum Principle (A-PMP). The A-PMP optimizes gearshift commands and torque distribution for overall energy efficiency. As a practical numerical solution in PMP, the proposed methodology utilizes a piecewise linear approximation of the engine fuel rate and state of charge (SOC) derivative by considering drivability and fuel economy simultaneously. Moreover, battery aging is explicitly studied by introducing a control-oriented model, which aims to investigate the effect of battery aging on the optimization performance in the development of the HEVs. An approximate energy management strategy with piecewise linear models is then formulated by the A-PMP, which targets a better performance for the Hamiltonian optimization. The gearshift map is extracted from the optimal results in the standard PMP to hinder frequent gearshift by considering both drivability and fuel economy. Utilizing an approximated Hamilton function, the torque distribution, gearshift command, and the battery aging degradation are jointly optimized under a unified framework. Simulations are performed for dynamic programming (DP), PMP, and A-PMP to validate the effectiveness of the proposed approach. The results indicate that the proposed methodology achieves a close fuel economy compared with the DP-based optimal solution. Moreover, it improves the computation efficiency by 50% and energy saving by 3.5%, compared with the PMP, while ensuring good drivability and fuel efficiency.