This paper takes a light commercial truck as the subject of research, developing a carbon emission calculation model based on the life cycle theory. The model sets its boundaries at the stages of raw material acquisition, production and transportation, parts manufacturing and vehicle assembly in the automobile production process. The paper also explores the differences in the life cycle carbon emissions of the materials involved in the lightweighting measures, and compares the carbon emissions of the vehicle before and after lightweighting. The results show that the life cycle carbon emissions of the substitute materials such as aluminum, magnesium, and carbon fiber reinforced plastic are significantly higher than those of the substituted materials, steel and cast iron. The emissions are quantified as 6.23 kg/kg for forged aluminum, 6.92 kg/kg for cast aluminum, 14.76 kg/kg for magnesium products, 20.2 kg/kg for carbon fiber reinforced plastic, 2.85 kg/kg for ordinary steel, 0.67 kg/kg for stainless steel, and 0.81 kg/kg for cast iron. After lightweighting, the carbon emissions from the powertrain system, driveline system, chassis, and body parts increased by 0.57%, 525.51%, 11.57%, and 33.29%, respectively, leading to a total increase in the vehicle's lifecycle carbon emissions by 36.22%. Both steel and aluminum have lower lifecycle carbon emissions, which results in more significant carbon reduction effects in the vehicle body parts before and after lightweighting.

To solve the problem of invalid data during the dew point protection phase of NO_x sensors in the remote monitoring of heavyduty vehicles, the paper used the PEMS tests on a China VI heavyduty vehicle to investigate the high NO_x, emissions during this protection period. Furthermore, the feasibility of using a neural network algorithm to repair the data and improve the utilization rate of remote monitoring data was verified. The results show that the dew point protection leads to more than 30% NO_x, emissions not being recorded. During this protection phase, over 90% of the data revealed that the vehicle speed was below 54 km/h, the engine coolant temperature was below 82 °C, the SCR inlet temperature was below 245 °C, and the SCR outlet temperature was below 225 °C. The neural network algorithm effectively repaired the invalid NO_x, measurements during dew point protection, with errors of less than 4%.

As one of the most commonly used modes of transportation, the majority of automobiles are in the L2 to L3 stage of humanmachine codriving technology development. Before the emergence of L5level autonomous driving technology, “humanmachine codriving” remains the dominant driving method, with its various invehicle systems and interaction methods continuously being improved. The integration of multimodal interaction with automotive technologies is bound to ignite new "sparks" as a future design trend. This paper firstly summarizes the research on multimodal interaction design for invehicle systems, covering directions such as fatigue warning, collision warning, lane departure warning, intelligent takeover reminder, and intelligent parking. Subsequently, it analyzes the natural interaction methods of invehicle AI multimodal interaction design, including multiscreen interaction, touch interaction, gesture interaction, voice interaction, facial expression interaction and eye movement interaction. The article uses literature research and case studies to explore how to improve the driver's comfort in the context of safety and emotional aspects, and anticipates the applications and future trends of multimodal interaction design for invehicle systems. Finally, it is concluded that the integration of appropriate and effective interaction methods will improve the safety and driving comfort of various invehicle systems and applications. The introduction of multimodal interaction is destined to become a trend in automotive development.

The diagnosis of power battery faults is crucial for the normal operation of electric vehicles. In response, this paper proposes a power battery fault diagnosis method using local mean decomposition and the local outlier factor, aimed at fault recognition and localization within battery packs. Firstly, the voltage signal is preprocessed through local mean decomposition, followed by the reconstruction of the voltage signal according to the correlation coefficient. Furthermore, the kurtosis factor of the reconstructed signal is extracted as the fault feature input to the local outlier factor algorithm, which then identifies the faulty battery based on an adaptive threshold. Finally, the proposed method is validated on a real vehicle, effectively and accurately detecting faults while demonstrating the reliability and robustness of the method.

To improve the effectiveness of intrusion detection systems against tampering attacks in the power domain of new energy vehicles, a power domain protection model is established, including both association rule detection and outlier detection. By collecting the power domain messages from the actual vehicles and establishing a rule base using the association rule algorithm, this model aims to detect tampering attacks. On the basis of association rule detection, complex types of tampering attacks are identified through outlier detection. The simulation results show that this method improves the detection accuracy by 5.83% compared to traditional association rule methods, effectively detecting tampering attacks in the power domain of new energy vehicles.

This study focuses on a smallsized electric passenger vehicle equipped with a heat pump system, conducting a driving range test under lowtemperature CLTCP cycle conditions. By comprehensively examining the test data and analyzing the vehicle's energy flow, potential avenues for improving the driving range are explored. A comprehensive model of vehicle dynamics and economics, including the thermal management system, is established on the Amesim platform. After calibration, different optimization schemes are simulated and compared to develop a combined optimization scheme. Experimental results show that the combined optimization scheme can improve the lowtemperature driving range by 12.6%. Among them, the contribution of the thermal management system optimization scheme significantly surpasses that of the vehicle resistance optimization scheme and the control strategy optimization scheme. This study provides reference ideas and methods for improving the driving range of pure electric passenger vehicles under lowtemperature environments.

To improve the crashworthiness of automobile energyabsorbing boxes, the paper proposes three different biomimetic doublediamondribbed multicell thinwalled structures, inspired by the microstructure of bamboo. Initially, the finite element model for these biomimetic structures is established and its impact resistance is compared with that of the traditional octagonal multicell thinwalled structure through finite element simulations. Subsequently, the paper analyzes the effects of the arrangement of doublediamond ribs and the thickness of inner walls on the energy absorption characteristics and deformation modes of the novel structures. The results indicate that the biomimetic structures offer significantly better energy absorption than their traditional counterparts. The arrangement of doublediamond ribs and the thickness of both inner and outer walls affect the structural energy absorption characteristics. With the increase in the inner wall thickness, the initial peak force on the structure decreases, but the total energy absorption and specific energy absorption decrease, leading to a reduction in load stability. The proposed biomimetic doublediamondribbed multicell thinwalled structure effectively reduces the injuries to occupants in frontal vehicle collisions, and can be applied in the design and development of energyabsorbing boxes for new energy vehicles.

This paper aims to investigate the effects of speed on the kinematic response and injuries of riders in frontal collision accidents between trucks and twowheelers. For these purposes, multibody models of the truck and twowheeled vehicle were established using MADYMO software to reconstruct a frontal collision involving these vehicles. Furthermore, 25 accident simulations were performed at different speeds by employing the validated model. The kinematic responses and injury metrics were analyzed for twowheeler riders. The results indicate that the extent of body rotation in riders increases with the speed of both the twowheeler and the truck. The riders face a high risk of severe head and chest injuries when the truck's impact speed exceeds 20 km/h, and a notable risk of serious lower extremity injuries when the truck speed exceeds 25 km/h. At truck speeds between 30 km/h to 40 km/h, there is a trend of increasing HIC values for the rider's head with an increase in twowheeler speed, while the trend for chest acceleration is opposite.

In response to the issue of vehicle brake jitters commonly seen in electrichydraulic composite braking systems driven by multiaxis distributed motors, two strategies are proposed: a motor braking power correction strategy during the pressure buildup stage and a coordinated control strategy based on feedforwardfeedback. These strategies respectively address brake jitter by coordinating the composite braking force during the pressure buildup phase and other stages. A PID controlbased strategy for ABS has been developed to resolve the braking conflict arising from the simultaneous operation of the ABS and the motor braking system by adjusting the motor braking force. The effectiveness of the proposed approach was validated by conducting a comprehensive joint simulation using TruckSim, Matlab/Simulink, and AMESim. Results show that the brake jolt decreases by 20.66% during the pressure buildup phase and 92.59% during the motor exit phase, significantly improving the overall driving experience. Furthermore, the ABS control strategy facilitates the recuperation of braking energy while maintaining the ideal slip ratio. Supported by the full vehicle braking test results, the coordinated control strategy achieves efficient recuperation of braking energy while ensuring good braking performance.

To address the challenge of multiwaypoint delivery by unmanned vehicles in scenarios such as industrial parks, the paper proposes a lanelevel global path planning, generation and tracking control method based on vectorized highdefinition maps. Considering the influence of delivery point sequencing on the total path length, the A* algorithm is used based on highdefinition maps to calculate the optimal path between each delivery point. And then, the dynamic programming algorithm is employed to obtain the globally optimal path that passes through multiple delivery points. The planned path is smoothed using Bezier curves, and the reference driving speed is set according to the road curvature at different points along the path, thereby creating a lanelevel target trajectory suitable for tracking. Subsequently, a model predictive controller based on a twodegreeoffreedom vehicle model is designed for trajectory tracking to achieve autonomous control of lowspeed logistics delivery vehicles. The proposed planning and control method is tested on a joint simulation platform of CarSim, PreScan and Simulink, as well as on a real vehicle platform. The results show, compared with the traditional path determined based on the nearest delivery point strategy, that the path length determined by the proposed method is reduced by an average of 6.15%. The developed trajectory tracking controller ensures that the lateral deviation of the experimental delivery vehicle from the target trajectory is maintained within 0.25 m and the yaw angle deviation is kept within 5°.