The worldwide drive towards low-carbon transportation has made Hybrid Electric Vehicles (HEVs) a crucial component of sustainable mobility, particularly in areas with limited charging infrastructure. The core of HEV efficiency lies in the Energy Management Strategy (EMS), which regulates the energy distribution between the internal combustion engine and the electric motor. While rule-based and optimization methods have formed the foundation of EMS, their performance constraints under dynamic conditions have prompted researchers to explore artificial intelligence (AI)-based solutions. This paper systematically reviews four main AI-based EMS approaches—the knowledge-driven, data-driven, reinforcement learning, and hybrid methods—highlighting their theoretical foundations, core technologies, and key applications. The integration of AI has led to notable benefits, such as improved fuel efficiency, enhanced emission control, and greater system adaptability. However, several challenges remain, including generalization to diverse driving conditions, constraints in real-time implementation, and concerns related to data-driven interpretability. The review identifies emerging trends in hybrid methods, which combine AI and conventional optimization approaches to create more adaptive and effective HEV energy management systems. The paper concludes with a discussion of future research directions, focusing on safety, system resilience, and the role of AI in autonomous decision-making.

With the rapid development of autonomous driving, robotics, and intelligent transportation, multi-sensor-based environment sensing technology for intelligent vehicles has become a popular research direction. In order to better fuse the data acquired by multi-sensors, accurate external parameter calibration becomes one of the critical issues. According to the method of external parameter calibration, this paper first introduces the offline calibration technology based on target and targetless methods. However, once these two methods change the relative position between the camera and the LiDAR, it can only be returned to the field to re-calibrate. The computational complexity is high, which makes it necessary to use the online calibration directly. Hence, this paper follows up with the introduction of online calibration technology based on deep learning. Unlike previous methods that need to extract features from calibration boards or environments, various types of networks can directly learn the mapping relationship between images and point clouds, From the calibration results, the average error of translation and rotation of traditional methods can reach 0.34cm and 0.45°, the average error of using deep learning networks such as LccNet, which is the most widely used in existing networks and has good calibration effect, can reach 0.297cm and 0.017°. Compared with the traditional method, the accuracy of online calibration technology is respectively improved by 12.6% and 96.2%, which shows the results of online calibration technology are better than the traditional offline method, and there are some recently proposed methods incorporate an attention mechanism and use an optimization algorithm instead of a loss function to refine the outer parameters. From the review, learning the relative relationships between sensors through neural networks works best, and the process is relatively free of human intervention. Contrary to the existing reviews, this paper provides a general structure of calibration methods universally used in various environments and compares various methods based on this general structure.

The vehicle routing problem with time windows (VRPTW) remains a formidable challenge, due to the intricate constraints of vehicle capacity and time windows. As a result, an algorithm tailored for this problem must demonstrate robust search capabilities and profound exploration abilities. Traditional methods often struggle to balance global search capabilities with computational efficiency, thus limiting their practical applicability. To address these limitations, this paper introduces a novel hybrid algorithm known as large neighborhood search with modified rat swarm optimization (LNS-MRSO). Modified rat swarm optimization (MRSO) is inspired by the foraging behavior of rat swarms and simulates the search process for optimization problems. Meanwhile, large neighborhood search (LNS) generates potential new solutions by removing and reinserting operators, incorporating a mechanism to embrace suboptimal solutions and strengthening the algorithm’s prowess in global optimization. Initial solutions are greedily generated, and five operators are devised to mimic the position updates of the rat swarm, providing rich population feedback to LNS and further enhancing algorithm performance. To validate the effectiveness of LNS-MRSO, experiments were conducted using the Solomon VRPTW benchmark test set. The results unequivocally demonstrate that LNS-MRSO achieves optimal solutions for all 39 test instances, particularly excelling on the R2 and RC2 datasets with percentage deviations improved by 5.1% and 8.8%, respectively, when compared to the best-known solutions (BKSs). Furthermore, when compared to state-of-the-art algorithms, LNS-MRSO exhibits remarkable advantages in addressing VRPTW problems with high loading capacities and lenient time windows. Additionally, applying LNS-MRSO to an unmanned concrete-mixing station further validates its practical utility and scalability.

Lateral stability is vital to vehicle handling stability and traffic safety. It is also a crucial factor for the path-tracking ability of the vehicle in the intelligent transportation system (ITS). Most of the research focuses on vehicles with an Ackerman steering system. The ASV (articulated steering vehicle) has a lower steering radius. Thus, it is widely used in some special vehicles, such as mining and construction vehicles. The ASV has weaker lateral stability than the vehicle with an Ackerman steering system. To improve the stability of an ASV, the nonlinear lateral dynamic model is established and validated by field test. With the lateral dynamic model, the steering characteristic of the ASV is analyzed. Based on the stability criteria analysis, the vehicle sideslip angle and angular velocity phase portrait are chosen as the stability indicator. An integrated AASS (active articulated steering system) DYC (direct yaw control) controller based on the adaptive MPC (model predictive control) method is designed according to the track on the phase plane. The double lane-change and 0.7 Hz sine with dwell maneuver are initiated based on the integrated vehicle dynamic model. The results suggest that the provided controller has a better stability performance than the current antiwindup PID control algorithm. It lays a good foundation for the vehicle safety and path tracking of ASV in the ITS.

The robotic milling of automotive casting components can effectively reduce human participation in the production process and enhance production efficiency and quality, but the premise addresses the reasonable planning of machining paths. To address major challenges, this paper proposes a spatial path planning method for the robotic milling of casting flash and burrs on an automotive engine flywheel shell based on the optimal machining posture. Firstly, an improved stereolithography slicing algorithm in arbitrary tangent plane direction is put forward, which solves the problem that the existing stereolithography slicing algorithm cannot accurately extract the contour of complex components. Secondly, the contour path curve fitting of the slicing points of the flywheel shell is realized based on the B-spline curve. Next, a machining posture evaluation function is established based on the robot’s stiffness performance, and the optimal machining posture is solved and verified with simulation according to the machining posture evaluation function and posture interpolation. Finally, the experiments indicate that the proposed method can significantly enhance the machining quality, with an average allowance height of 0.33 mm, and reduce the machining time to 9 min, compared with the conventional manual operation, both of which satisfy the machining requirements.