Vehicles

This work addresses the challenges of airline planning, which requires the integration of flight scheduling, aircraft availability, and maintenance to ensure both airworthiness and profitability. Current solutions, often developed by human experts, are susceptible to bias and may yield suboptimal results due to the inherent complexity of the problem. Furthermore, existing state-of-the-art approaches often inadequately address critical factors, such as maintenance, variable flight numbers, discrete time slots, and potential flight repetition. This paper presents a novel approach to aircraft routing optimization using a model that incorporates critical constraints, including path connectivity, flight duration, maintenance requirements, turnaround times, and closed routes. The proposed solution employs a simulated annealing algorithm enhanced with specialized perturbation operators and constraint-handling techniques. The main contributions are twofold: the development of an optimization model tailored to small airlines and the design of operators capable of efficiently solving large-scale, realistic scenarios. The method is validated using established benchmarks from the literature and a real case study from a Mexican commercial airline, demonstrating its ability to generate feasible and competitive routing configurations.

This paper presents a coordinated control strategy for an electro-hydraulic composite braking system in in-wheel motor electric vehicles to enhance regenerative energy recovery and braking safety. A novel hydraulic control unit (HCU) without a pressure-reducing valve is designed to simplify structure and maximize energy utilization. Based on the ideal braking force distribution, a force allocation strategy coordinates motor and hydraulic braking across modes, ensuring motor torque can compensate total braking torque when wheel lock occurs. An anti-lock braking (ABS) strategy relying solely on motor torque adjustment is proposed, keeping hydraulic torque constant while rapidly stabilizing slip within 13–17%, thereby avoiding interference between hydraulic and motor braking. A joint Simulink–AMESim–CarSim platform evaluates the strategy under varying conditions, and real-vehicle tests in regenerative mode confirm feasibility and smooth switching. Results show the proposed approach achieves target braking intensity, improves energy recovery, reduces torque oscillations and valve actions, and maintains stability. The study offers a practical solution for integrating regenerative braking and ABS in in-wheel motor EVs, with potential for hardware-in-the-loop validation and advanced stability control applications.

It is of great significance to construct a networked energy-saving driving strategy method and application framework to solve the problems of traffic disorder, speed fluctuations, and high energy consumption caused by frequent acceleration, deceleration, and lane changing of vehicles in road sections with variable traffic flow. Considering the mixed traffic scenario where autonomous vehicles and manually driven vehicles interact and infiltrate, a hybrid traffic flow vehicle energy-saving driving model was established, and the Dueling Double Deep Q-Network (D3QN) was used to optimize and solve the energy-saving driving model; Selecting Qingdao urban intersections as application scenarios, energy-saving driving strategy application facilities were constructed in simulation experiments to carry out simulation verification of energy-saving driving strategies for mixed traffic flow in the context of vehicle networking. The simulation results show that in different scenarios with different proportions of CAVs, the energy-saving strategy based on D3QN deep reinforcement learning algorithm can achieve fuel savings of 8.41%~6.67% compared to conventional strategies. Compared with the ordinary reinforcement learning algorithm Q-learning, its fuel saving rate is increased by 1.94%~1.5%, and the energy-saving effect becomes more significant with the increase of traffic density; From the perspective of dynamic characteristics, the speed stability under the control of D3QN algorithm is superior to Q-learning algorithm, and significantly better than conventional strategies, further highlighting the comprehensive advantages of D3QN algorithm in optimizing traffic flow status and energy consumption control. The energy-saving driving strategy in the networked environment can reduce fuel consumption caused by speed fluctuations and traffic flow frequency disturbances, and optimize the stability of traffic flow operation.