To improve the traffic flow efficiency of vehicleroad cooperative adaptive cruise vehicles at signalized intersections, a model predictive control algorithm considering traffic signal status is proposed. When following a vehicle through an intersection, the vehicle first uses V2X to obtain information about the traffic signal, including its position, color and countdown timer. The vehicle's longitudinal kinematics model is used to link the acceleration request from the controller to the vehicle's future state. An objective function considering both the vehiclefollowing state and the traffic signal is designed. Finally, the output acceleration request is calculated through nonlinear optimization, simulating the behavior of human drivers when following other vehicles through signalized intersections. The simulation results show that compared with ACC vehicles, the vehicle using this algorithm achieves higher traffic efficiency while maintaining safety when passing through signalized intersections. This research provides a theoretical basis for prioritizing efficiency in the design of CACC vehicle control algorithms, and offers practical insights for developing an efficiency mode in systems that include both economic and efficiency modes.

In the process of designing automobile structures and components, a series of optimization is required to achieve optimal performance, the lightest weight, and the highest efficiency. Due to the complexity of optimization problems, the heuristic intelligent optimization algorithms are typically used to solve them. However, the mechanisms of the heuristic optimization algorithms are not well understood, and their effectiveness in optimization design of automobile parts have not been fully studied. Therefore, selecting appropriate algorithms for different problems is challenging. In this paper, representative algorithms were derived and expressed uniformly. Fiftytwo sets of mathematical benchmark functions and five automobile parts optimization design cases were tested. The results show that the two types of hybrid improved algorithms perform well in the optimization design of automobile parts. Recommendations for engineering

In order to improve the health of charging piles, a charging scheduling strategy for electric vehicles based on pile health status is proposed. The strategy begins with a comprehensive assessment model that evaluates charging pile health through electrical and safety performance indicators, using 6 core modules and 26 indicators as the evaluation framework. Secondly, considering the impact of charging load on pile health, a charging optimization model based on pile health status is established. Charging load is standardized by converting it into charging time, allowing operators to plan vehicle charging locations using both charging time and pile health as guiding factors. Finally, the NSGAII algorithm is employed to solve the model, with the charging pile health model used for evaluation. Parameters and charging data from 10 charging piles in a region of Jiangsu Province are selected for verification, and two different optimization scenarios are set up for comparison. The results show that, compared to the other two scenarios, the proposed strategy and algorithm increase the annual health of charging stations by 18.54%, improve the average annual health of charging piles by 1.85%, and reduce the maximum monthly health variance of charging stations to 0.001 5.

To address the issue of deviations from dayahead plans caused by the uncertainty in output from largescale electric vehicles (EVs) and wind power in the realtime energy and frequency regulation (FR) market, the paper proposes a realtime bidding model. The model incorporates a powercapacity deviation assessment mechanism to enable EVs and wind power to participate effectively in the energyFR market. A dynamic scheduling boundary model and a rapid power allocation model for electric vehicles are established to ensure that EV agents can make timely decisions in the realtime market. A powercapacity deviation penalty mechanism is introduced based on the game dynamics among market participants. A bidding strategy is proposed for EVs and wind power in the realtime energyFR market, and a twolevel optimization model is established. The upperlevel objective minimizes the deviation assessment costs for individual EVs or wind power units, while the lowerlevel objective minimizes the overall system operating cost. An example analysis is conducted to examine the dynamic boundaries and power allocation results for EVs, as well as the impact of the deviation assessment mechanism on the intraday optimization and bidding strategies of EVs and wind power. The results show that the proposed strategy achieves realtime power balance while optimizing returns for both.

With the rapid development of advanced intelligent connected vehicles, their safety has become a widespread concern. Currently, the mechanisms to ensure functional safety are a major research focus both domestically and internationally. Countries are gradually establishing standards for vehicle safety performance, and the International Organization for Standardization (ISO) continues to refine the Functional Safety (ISO 26262) and Safety of the Intended Functionality (ISO 21448) standards. On this basis, the safety objectives of most automotive products, especially intelligent chassis systems related to autonomous driving, such as brakebywire and steerbywire actuators, are classified under Automotive Safety Integrity Level D (ASIL D). This paper summarizes industry research on redundancy mechanisms in system safety architecture, analyzes and organizes failoperational safety architecture across different levels of autonomous driving, and examines functional safety degradation strategies. Finally it discusses the potential for integrating ISO 21448 into the ISO 26262 standard to effectively prevent accidents due to errors in the vehicle's Electronic/Electrical (E/E) systems and insufficient actuator performance, thereby providing stronger assurance for safe vehicle operation.

To improve the safety and comfort of autonomous vehicles during lane changes, the proposed approach incorporates the impact of lanechanging on local traffic flow and introduces a lanechanging inertia factor based on traditional decisionmaking models. To overcome the limitations of decoupled longitudinal and lateral trajectory planning, a joint constraint planning approach is proposed. Using dynamic programming and quadratic programming algorithms, the current lateral trajectory curvature is adjusted based on the longitudinal constraints from the previous frame. In the longitudinal planning process, key obstacles are filtered based on the current lateral planning results, and curvaturebased speed constraints are

The LCL motor inverter is modeled using the complex vector in the synchronous rotating reference frame (SRF), and the influence of the overall phase margin and coupling performance of the system under different control strategies is thoroughly investigated. The analysis reveals that the inverter has a coupling relationship between the dq axes, and the active damping feedback branch also intensifies the coupling phenomenon. To address these issues, a complex vector decoupling strategy is proposed. The decoupling components for the active damping feedback branch and the current loop system are sequentially designed. The decoupling elements are applied to both the feedback branch and the output current of the PI controller respectively, which not only effectively eliminate the dynamic coupling but also enhances the system's dynamic performance. Finally, the effectiveness of the control strategy is verified by comparing the simulation and experimental results with the traditional decoupling control.

In methanol engines, mixedgas compression ignition can be achieved by increasing the compression ratio, using spark ignition, and adopting polyoxymethylene dimethyl ethers (PODE) as the active improved fuel. This approach overcomes the limitations imposed by flame propagation speed on thermal efficiency. Experiments were conducted to investigate and compare the combustion characteristics, thermal efficiency and emission properties of dualfuel sparkassisted compression ignition (SACI) mode and singlefuel methanol ignition at different loads and engine speeds. The main conclusions are as follows: the effect of dualfuel SACI on thermal efficiency varies with the load range. At a BMEP of 0.8 MPa, the thermal efficiency reaches 41.9%, an increase of 1.9% compared to methanol ignition. However, at BMEP levels below 0.4 MPa, the cyclic variability is higher and the thermal efficiency is lower than that of methanol ignition. Dualfuel SACI reduces NO, emissions, achieving a 79% reduction compared to methanol ignition at a BМЕР of 0.8 MPa. Increasing RPM accelerates PODE diffusion, aiding the formation of the initial combustible mixture, decreasing the proportion of compression ignition in combustion, and enhancing the proportion of flame propagation ignition. At low loads, HC emissions decrease with higher RPM. However, high RPM reduces the compression ignition ratio and increases NO, emissions. Low RPM and high load conditions are more favorable for efficiency and emissions. These findings provide valuable support for the application of methanol engines.

In response to the challenges posed by the widespread adoption of fast charging in lithiumion battery health assessment, this study develops a stateofhealth estimation model for dynamic fastcharging scenarios. Twelve direct features are extracted from the partial voltage curve during the fast charging process, followed by a comprehensive analysis of degradation mechanisms strongly correlated with these features. Subsequently, feature selection is conducted based on degradation mechanisms and correlation analysis, and the radial basis function neural network (RBFNN) is deployed to establish the estimation model. The validation results indicate that the constructed data features exhibit excellent generalization across various battery degradation paths, improving accuracy by over 17% compared to traditional feature selection methods. Satisfactory estimation results are obtained even under different fast charging protocols and with a smaller training dataset.

This paper proposes an intelligent multiclass fault diagnosis algorithm for the diesel engine fuel system based on PCA and ELM. Firstly, a fault diagnosis model is established in Simulink, utilizing real vehicle data to support offline verification. Subsequently, an architecture for the intelligent fault diagnosis algorithm is designed, following the OSACBM standard. Based on this architecture, the online fault diagnosis algorithm is developed in the Simulink and tested using a hardwareintheloop (HIL) platform. The verification results show that the proposed intelligent multiclass fault diagnosis algorithm achieves high accuracy and reliability in both offline simulations and HIL testing, indicating its potential for invehicle applications.