On the basis of the analysis of vibration characteristics of motor bearing faults and the influence from testing noise on site, calculation of the symptom parameters representing the vibration signals according to the measured vibration signals is proposed, and the sensitivity analysis is carried out to these parameters which can refine effective symptom parameters. As there are limitations for motor bearing fault intelligent diagnosis methods based on genetic algorithm and neural network, while Bayesian network has a good learning, inference and astringency. Therefore, the effective combination of the symptom parameters and Bayesian network is made and a new intelligent diagnosis method is posed

This paper presents a control method based the lateral interconnected air suspension system, in order to improve the road handling of vehicles. A seven-DOF (Degree of freedom) full-vehicle model has been developed, which considers the features of the interconnected air suspension system, for example, the modeling of the interconnected pipelines and valves by considering the throttling and hysteresis effects. On the basis of the well-developed model, a sliding mode controller has been designed, with a focus on constraining and minimizing the roll motion of the sprung mass caused by the road excitations or lateral acceleration of the vehicle. Moreover, reasonable road excitations have been generated for the simulation based on the coherence of right and left parts of the road. Afterwards, different simulations have been done by applying both bumpy and random road excitations with different levels of roughness and varying vehicle lateral accelerations. The simulation results indicate that the interconnected air suspension without control can improve the ride comfort, but worsen the road handling performance in many cases. However, by applying the proposed sliding mode controller, the road handling of the sprung mass can be improved by 20% to 85% compared with the interconnected or non-interconnected mode at a little cost of comfort.

Suspension control systems are in need for more information of road roughness conditions to improve their performance under different roads. Existing methods of gauging road roughness are limited, and they usually involve visual inspections or special vehicles equipped with instruments that can gauge physical measurements of road irregularities. This paper proposes data collection for a period of a time from accelerometers fixed on unsprung mass and uses the mean square values of this datasets divided by vehicle speed to classify the roughness conditions of a section of a road. This approach is possible due to the existence of relationships between the power spectral densities of the road surface, unsprung mass accelerations via a transfer function, and vehicle speed. This paper gave the relationship between the resolution of road roughness classification and the length of time‐window and suggestions about choosing the appropriate time‐window length on the balance of road roughness resolution and classification delay. Moreover, to enhance the stability of classification, the influence of damping parameters of vehicle suspension on the classification output is studied, and a classification method of road roughness is proposed based on neural network and damping coefficient correction.

In unanticipated obstacle scenarios at intersections, the safety and mobility of autonomous vehicles (AVs) are negatively impacted due to the conflict between traffic law compliance and obstacle avoidance. To solve this problem, an obstacle avoidance motion planning algorithm based on artificial potential field (APF) is proposed. An APF-switching logic is utilized to design the motion planning framework. Collision risk and travel delay are quantified as the switching triggers. The intersection traffic laws are digitalized and classified to construct compliance-oriented potential fields. A potential violation cost index (PVCI) is designed according to theories of autonomous driving ethics. The compliance-oriented potential fields are reconfigured according to the PVCI, forming violation cost potential fields. A cost function is designed based on compliance-oriented and violation cost potential fields, integrated with model predictive control (MPC) for trajectory optimization and tracking. The effectiveness of the proposed algorithm is verified through simulation experiments comparing diverse traffic law constraint strategies. The results indicate that the algorithm can help AVs avoid obstacles safely in unanticipated obstacle scenarios at intersections.

Fuel cell hybrid vehicles offer significant potential in heavy-duty transportation due to their high efficiency, extended range, and zero emissions, making them a key enabler of sustainable transportation. To enhance the energy consumption economy and lifecycle economy of fuel cell hybrid mining trucks (FCHMTs) while reducing total operating costs and promoting environmental sustainability, this paper proposes an approximate globally optimal energy management strategy (EMS) based on a rule-interposing balance cost minimization strategy (AGO-BCMS). First, an FCHMT power system model is established, including degradation models for the fuel cell and battery. Then, the global optimality of dynamic programming (DP) is utilized to extract the fuel cell output characteristics under different battery states and vehicle power demands. Subsequently, optimal rules are designed and embedded into the cost minimization optimization model to plan the fuel cell output range under actual driving conditions. Simultaneously, dynamic threshold updates are performed based on vehicle driving condition phase recognition. Finally, energy distribution optimization is calculated using sequential quadratic programming (SQP). This strategy not only improves the economic viability of FCHMTs but also contributes to the broader goals of advancing sustainable transportation solutions. The proposed strategy was validated under both single round-trip and continuous operational conditions. Simulation results show that, under single round-trip conditions, the proposed strategy reduces the total operational cost by 3.13%, 4.09%, and 10.90% compared to balance cost-minimization strategies (BCMS), equivalent consumption minimization strategy (ECMS), and rule-based strategies, respectively. Under continuous operational conditions, the total cost is reduced by 3.61%, 6.63%, and 15.90%, respectively.

The suspension system is a significant part of a vehicle because it transmits the torque and force between the wheels and the frame, meeting the requirements of ride comfort. In this paper, a novel interconnected air suspension was introduced and a cooperative control between interconnected mode activation (IMA) and outsourced mode activation (OMA) was designed. To improve ride comfort in a bus at a minimal energy consumption, this cooperative control based on model predictive control (MPC) for computing the best force and the rule was defined to distribute the best suspension force generated by IMA and OMA. The simulation and experimental results showed that the proposed control strategy significantly improved ride comfort in different conditions. Compared with traditional control in the test vehicle, the RMS of the front-left sprung mass and unsprung vertical acceleration decreased by 24.2% and 14.5%, respectively, under a straight condition; the RMS of the sprung (unsprung) mass vertical acceleration reduced by 22.38% and 15.43%, respectively, under a bump condition.