In response to the insufficient heat dissipation and poor surface temperature uniformity of the battery pack in traditional liquid cooling systems, this study proposes a novel battery pack structure based on a hybrid cooling strategy combining air cooling and liquid cooling. A threedimensional model of the designed structure is established using Catia software, and its cooling simulation performance is analyzed using Fluent software. The research results indicate that compared to a single liquid cooling structure which exhibits overheating issues at 2 C and 2.5 C, the hybrid air and liquid cooling structure can effectively control the maximum temperature and the maximum temperature difference within 45 °C and 5 °C, respectively, across different discharge rates. Furthermore, the influence of different fluid inlet velocities on the battery pack cooling performance is investigated. By selecting the optimal combination of wind speed at 5 m/s and coolant flow rate at 0.5 m/s, and then implementing targeted optimization of the flow channel, the maximum temperature of the battery pack is further reduced from 28.12 °C to 27.45 °C under the same operating conditions. This novel structure provides an innovative direction for subsequent approaches in battery thermal management design.

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%.

Electrification has emerged as the main trend in new energy vehicles over the past decade. With the increasing number of EVs, reducing their energy consumption aligns with the national strategy for energy conservation and emission reduction, and also improves user's driving experience. The paper first analyzes actual road driving data from an electric SUV. Through dimensional reduction and correlation analysis, the acceleration variance is found as the key factor influencing energy consumption. Furthermore, three typical realroad operating conditions, i.e, constant speed driving, tip in/tip out, and ramp, are chosen for a parametric study by using a 1D model. The tradeoff between energy and time consumption is also discussed. Finally, several principles are established for ecodriving considering the acceleration and speed of vehicles, which include reducing speed during highway cruising, ensuring smooth driving in urban congestion, and utilizing vehicle inertia and minimizing regenerative braking on ramps.

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.

During the actual road driving test process, strictly controlling the driving state of heavyduty trucks can be challenging, which makes it difficult to directly evaluate the energy consumption levels from the test results. Therefore, this paper proposes an energy consumption evaluation method based on the division of driving characteristic intervals. Firstly, by analyzing the distribution characteristics of test points in the China Heavyduty Commercial Vehicle Test Cycle for Truck (CHTCHT), a division scheme for the number and boundaries of speed and acceleration intervals for heavy duty trucks is proposed. Then, considering the impact of road slope, the actual road test data is filtered and used to calculate the average fuel consumption across different driving characteristic intervals. Finally, based on the cumulative driving mileage within each interval of the CHTCHT, the energy consumption evaluation results are obtained. The validation is conducted using three chassis dynamometer drum tests and two actual road driving tests. The results show that the proposed method improves the reproducibility of actual road energy consumption evaluation results for heavyduty trucks.

The battery pack, as the power source for new energy vehicles, is one of the most important components. The battery shell not only plays a vital role in protecting the battery package, but also contributes significantly to the vehicle's lightweight design, accounting for 2% to 6% of the total weight of the vehicle. Based on the global automotive industry's development goals for energy conservation and emission reduction, this paper discusses the development status quo of the new energy vehicle battery case industry, focusing on three aspects of safety, lightweighting and reliability. The common key technical challenges in this field are discussed, and the future development trends are proposed.

The operating environment for unmanned mining transport vehicles is challenging, characterized by unstructured roads such as highcurvature bends and slopes, which demand high requirements for unmanned transportation control. To improve the adaptability of traditional control algorithms like PID and to increase the accuracy of both lateral and longitudinal control in unmanned driving trajectory tracking, this study proposes a combined approach. It involves a multipoint preview lateral control method integrating pure pursuit with PID, and a longitudinal control method considering fuzzy control table parameter fitting. This approach is developed to reduce the number of control parameters while improving the algorithm's effectiveness. Initially, a basic controller is designed using the traditional control algorithm. And then the lateral and longitudinal control algorithms are developed based on the advantages of the basic algorithm. Finally, the performance of these algorithms is verified through hardwareintheloop simulation and onvehicle deployment testing. The experimental results show that compared with the Stanley method, the lateral control algorithm significantly improves vehicle path tracking accuracy. In terms of longitudinal control, the speed tracking error is less than 1 km/h, ensuring the smoothness and comfort of the vehicle's driving performance.

To ensure path tracking control precision in selfdriving vehicles with uncertain parameters, the paper proposes an outputfeedback control method with the prescribed control performance criteria. Firstly, by constructing the lateral offset error at the driver's preview point, a secondorder error integration system is established. Considering the unknown lateral velocity and perturbation in the tire cornering stiffness, a control model containing lumped unknown states is established by using the extended state method. The unknown states of the system are obtained by designing a linear extended state observer and the uniformly bounded convergence of the observation error is further proved. Then, to address the issue that the transient and steadystate performance of vehicles cannot meet the predefined accuracies, an output feedback path following controller with prescribed performance is proposed, incorporating observer estimations. The stability of the closedloop system is rigorously proved based on the Lyapunov theory. Finally, Matlab/

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°.