In order to solve the problems of low efficiency and large tracking errors in outdoor unmanned forklift path planning, a kinematic model of outdoor unmanned forklift is established. By constructing a path cost function and combining simulated annealing optimization, redundant nodes are removed to improve the search efficiency and path smoothness of the A* algorithm. On this basis, with speed and heading angle as input variables, a fuzzy controller is designed to dynamically adjust the preview distance of the pure tracking algorithm, and simulation and real vehicle experiments are carried out. The results indicate that the improved A* path planning algorithm reduces path length by 4.5% and turns by 98.7%. The dynamic preview distance pure tracking algorithm using fuzzy control reduces the maximum error by 58.7% in curve tracking, significantly improves path smoothness and tracking accuracy, and can be effectively applied to complex industrial application scenarios.

To address the problem that target vehicle trajectory prediction in complex traffic environments exhibits multi-modality and uncertainty, making it difficult for traditional model predictive control (MPC) to effectively utilize prediction information, a probabilistic risk prediction– based control method is proposed. The multi-modal trajectory prediction results are modeled in the probabilistic space to construct state probability distributions and extract their statistical characteristics. A confidence ellipsoidal risk envelope is generated and incorporated into the MPC framework as risk constraints. Experimental results demonstrate that the proposed method can effectively avoid collision risks while improving trajectory smoothness in complex scenarios, providing a feasible and generalizable solution for prediction-driven safety decision-making control of intelligent vehicles.

With the rapid development of new-energy vehicles, extending driving range and improving energy efficiency have become critical challenges. Predictive cruise control (PCC) leverages upcoming road information to optimize longitudinal control, thereby reducing energy consumption while enhancing driving comfort. Unlike previous studies that mainly focus on algorithmic improvement in simulation, this paper presents an engineering-oriented design framework that explicitly targets the constraints of an on-board vehicle control unit. The framework consists of three modules: environmental perception, path prediction, and control decision. By integrating dynamic programming (DP) with road-segment reconstruction, the system generates an optimal speed trajectory while fully utilizing grade-preview data. Simulation results over a mountainous highway scenario show a 3.24% reduction in energy consumption compared with a baseline human-driving profile. The results provide new insights and an engineering-ready reference for the subsequent real-vehicle implementation of intelligent energy-saving control in new-energy vehicles.

Aiming at the heterogeneous dual e-drive axle system composed of a distributed e-drive axle and a centralized e-drive axle, this paper proposes an inter-axle torque distribution control method based on instantaneous system efficiency optimization. The method first determines the system's maximum speed and peak driving force boundaries, constructs a comprehensive efficiency storage matrix. Then traverses all working conditions through a main speed loop and a driving force sub-loop. Then within the sub-loop, the driving force demand for each axle is calculated based on a preset wheel-edge force distribution ratio. Within their peak capability ranges, motor efficiencies are obtained by querying motor efficiency MAP, and the overall system efficiency is computed. Finally, the optimal system efficiency under each speed–total driving force operating point is screened and stored, along with its corresponding wheel-edge force distribution ratio, axle-specific wheel-edge forces, motor speeds, motor torques, and efficiencies. This method provides a theoretical basis for efficient cooperative control of heterogeneous dual e-drive axles.

Light-duty trucks are the main force in urban distribution, and their electrification process is the key to the low-carbon transformation of urban logistics. Charging light trucks have three major problems: slow charging, short range, and high cost, which severely restrict the large-scale promotion. However, battery swapping light trucks can simultaneously solve the problems of charging, range, and cost, and are the key to breaking the deadlock. This article conducts a systematic study on the matching design of the vehicle, battery, and battery swap station, and through comparative analysis and experimental verification methods, concludes that the single-pack fully automatic battery swapping system has more packs than the conventional battery swapping system, and the swapping efficiency can be increased by 50%, and the construction cost can be reduced by 50%. This is the current optimal battery swapping solution for light trucks. The solution is highly innovative and practical.

With the continuous development of the automotive industry, insufficient testing and validation during the product development phase have hindered the implementation of end of line test (EOL) procedures for complete vehicle inspection. To address the production issues caused by software-related electrical inspection problems during the trial production phase, as well as the lack of EOL diagnostic verification tools in the V-mode development process of vehicle development, this paper carries out the design and implementation of an EOL diagnostic tester based on unified diagnostic services (UDS) communication. Based on an analysis of the current state of industry EOL diagnostic testers and production base EOL testing, the EOL diagnostic tester is designed and developed. The interface of the EOL diagnostic tester is designed, and the hardware and software solutions for the tester are developed. Controller EOL verification is conducted through simulation testing and real vehicle testing. The research results show that the EOL diagnostic tester can achieve functions such as controller configuration file writing, module information reading, configuration word writing, system calibration items, static function testing, fault code clearing, and fault code reading. It can be used to avoid EOL issues caused by controller software during the development phase, providing a research approach for researchers in the field of EOL verification.

Aiming at the high-precision and low-latency requirements of ultra wide band (UWB) radar for child presence detection (CPD) in automotive systems, this paper proposes a child presence detection algorithm based on deep learning one dimensional convolutional neural network (1DCNN). The algorithm introduces the Fire module to optimize computational efficiency and improve detection accuracy. Meanwhile, to address the issues of high near-range Tap noise and correlation between adjacent Taps in UWB signals, specific Tap signals are selected for downsampling processing, and temporal features are incorporated to further enhance the algorithm's accuracy and real-time performance. Experimental results show that the algorithm occupies less than 30 KB of storage space in a low-computational-power, microcontroller unit (MCU) environment, achieving a test accuracy of 99.9%, which is significantly superior to traditional methods. This algorithm has been applied to in-vehicle child presence detection in an automobile and successfully passed the China new car assessment program (C-NCAP) child presence detection scenario benchmark test.

To address issues such as low debugging efficiency in real-world autonomous driving scenarios and difficulties for non-technical personnel in troubleshooting system anomalies, this paper is based on mature autonomous driving applications in closed campuses. It fully considers the different needs of research and development technicians and operational safety personnel by designing distinct user interfaces (UI). Information such as the status of the autonomous driving system and algorithm functionality is presented to in-vehicle personnel through User Interfaces, audible alarms, voice prompts, and other methods. In the event of any anomalies or emergencies, in-vehicle personnel can promptly receive status information, enabling technical staff to quickly identify the source of the anomaly. This ensures the safety of autonomous driving and improves the efficiency of debugging.

With the continuous acceleration of urbanization, urban public transportation systems are facing multiple pressures, including increasing passenger flow, complex operational scheduling, and inefficient resource allocation, making traditional development models inadequate for the needs of new smart city construction. This paper, grounded in the core development trends of the automotive industry"intelligentization, connectivity, and sharing", expands on the five-dimensional synergy of "people, vehicles, roads, stations, and cloud". It closely examines key directions such as autonomous driving applications, smart service upgrades, and shared ecosystem development to explore transformation strategies for public transportation. Through case study methods, the paper integrates platform operation data and user behavior data, compares technical selections, application scenarios, and performance outcomes across different regions, and quantifies data to validate the feasibility of the "five-dimensional expansion" model. This approach promotes deep integration between the automotive industry and public transportation systems, breaking the constraints of traditional isolated operations, delayed scheduling, and supply-demand mismatches in public transportation. It drives the industry from "passively responding to demand" to "proactively optimizing services" while fostering interdisciplinary collaborative research and development, provide theoretical references for building an efficient, green, and intelligent smart bus system.

To address the engineering challenge of balancing multiple conflicting performance objectives with lightweight requirements in the design of automotive rear hub brackets, this paper proposes a multi-objective topology optimization method based on the compromise programming approach. A maximum design space model is constructed based on suspension hardpoints, and a multi-objective optimization model is formulated with volume fraction and critical hardpoint stiffness as constraints, aiming to simultaneously minimize structural compliance and maximize the first natural frequency. Unlike conventional weighted sum methods, the compromise programming approach effectively resolves the non-convex conflicts between stiffness under multiple load cases and natural frequency by seeking a compromise solution with the shortest distance to the ideal point, thereby enabling rational material distribution. The optimization is performed using OptiStruct, followed by structural reconstruction and iterative design in Siemens NX. The validation results show that the optimized bracket exhibits superior stiffness at all hardpoints compared to the benchmark model, with a maximum stress of 201 MPa under extreme conditions, satisfying strength requirements. The mass is reduced from 5.27 kg to 3.15 kg, achieving a 40.2% weight reduction. The proposed method provides a transferable engineering pathway for the synergistic design of high performance and lightweight in critical chassis components.

The slip rate is a critical parameter for evaluating vehicle braking safety performance. To address the problem of absence of standardized methods and devices for the direct calibration of slip rate testers in China, this paper designs a standard slip rate calibration device capable of directly calibrating slip rate testers. Firstly, the design principles of the standard device are introduced. Next, the parameter-setting process during calibration is systematically analyzed. Subsequently, the experimental results undergo uncertainty evaluation to verify the device's reliability and accuracy. Finally, the device is used to test different models of slip rate testers. The experimental results demonstrate that the proposed standard slip rate calibration device fully meets the calibration requirements for slip rate testers. Compared with traditional speed-traceable indirect calibration methods, the standard slip rate device offers significant advantages, including simpler operation, higher efficiency, and comprehensive performance evaluation of the testers. This provides a new technical approach for the metrological traceability of slip rate testers.

In the line-haul logistics market for heavy-duty commercial vehicles, the trend toward high power paired with low speed ratios is becoming increasingly pronounced. To strike a trade-off point of customers power demands for high power with the pursuit of ultimate fuel efficiency for vehicles, this paper proposes an innovative method for assessing customers power needs across different power models. This approach is based on massive existing vehicle operation data. Specifically, it calculates the average operating power of vehicles within the same market segment and under identical operating conditions. The increase in this average power is used to quantify customers' psychological expectation for power improvement as power increases. Combining actual operating conditions, the optimal power matching is recommended. Analysis shows that under plain freeway condition and mountainous freeway condition, a unit power increase in corresponds to a rise in customer power demand of 0.09% and 0.12%, respectively. The findings indicate, in climb conditions, an 17 L, 588 kW model consumes 1.15 L/100 km less fuel and achieves a 7.9% higher speed than a 15 L, 500 kW model; under comprehensive conditions, the 17 L, 588 kW model maintains similar fuel consumption while achieving a 6.9% higher average speed than 15 L, 500 kW model.

The vibration fatigue problem of the chassis cantilever structure of trucks is prominent, which seriously affects user satisfaction as a production tool. The article focuses on the cracking problem of a heavy-duty truck's rear mudguard during durability testing, and conducts vibration fatigue simulation analysis. Combining modal and road spectrum energy distribution, the simulation results are analyzed and judged to be consistent with the actual vehicle problem, effectively reproducing the failure mode of the fault, and further application of size optimization techniques to determine improvement plans for complex variable cross-section structures. The proposed solution ultimately passed real-vehicle testing, demonstrating the effectiveness of the analytical method and providing a reference for addressing similar issues in chassis cantilever structural components of light and heavy trucks.

This study investigates the root cause of severe steering wheel vibration accompanied by low-frequency cabin boom in a light commercial vehicle during acceleration. Through resonance mechanism analysis, transfer path analysis, and modal identification/decoupling techniques, a systematic diagnosis is conducted. The results indicate that the steering wheel vibration primarily stems from resonance induced by coupling between the driveline bounce mode and the steering system mode. Based on these findings, engineering-feasible countermeasures are proposed, providing valuable practical insights for future noise, vibration, and harshness (NVH) development in similar vehicle models.

This paper addresses the problem of severe impact noise induced in the powertrain of a manual-equipped coach during transient clutch disengagement in reverse gear. A comprehensive dynamic model of the powertrain is developed using AMESim, which integrates the engine, transmission, drive shaft, rear axle and the lumped inertia of the entire vehicle. Combined with vehicle-level noise, vibration, harshness (NVH) test data, the underlying mechanism of the impact noise is revealed. The abnormal noise is primarily caused by the alternating switching between driving and driven conditions on the meshing interfaces of gears and splines along the power transmission path. Moreover, the impact response occurs earlier in the rear components of the powertrain than in the front components. Using the speed difference of the reverse idler gear as an evaluation index, simulation analyses are conducted to investigate the effects of clutch disengagement rate and internal clearances in the transmission on impact noise characteristics. Both simulation and experimental results demonstrate that lowering the clutch disengagement rate can significantly attenuate the powertrain impact noise. Gears located on the non-powertransmission paths of the transmission exert no influence on the impact noise. Merely reducing the backlash of transmission gears and splines provides only a limited improvement in suppressing the powertrain impact noise.

To real-time grasp the morphological changes during the battery test process, ensure test safety, improve data reliability, and achieve automated management to avoid equipment damage or personnel injury. Using at least one camera to capture real-time footage of the tested battery, arranging multiple images in chronological order according to their timestamps to obtain a sequence of images; Then, extract the position of the tested battery from the image sequence to obtain a sequence of image blocks; Next, for each image block in the sequence of image blocks, it is determined whether the edge has a complete boundary of a specific color, the image blocks with complete boundaries are deleted, and the remaining image blocks are concatenated and compressed to obtain a compressed packet; Finally, the compressed package is stored locally, and the test status is restored locally. The complete process of battery testing is reproduced through local restoration technology, achieving automatic and comprehensive monitoring of battery testing status. The experimental results show that the battery test status monitoring technology based on machine vision has lower storage space, storage time, bandwidth occupancy time, and resolution indicators than traditional methods.

To reduce the pores and cracks in the microstructure of the TC4 titanium alloy formed by selective laser melting (SLM), and to enhance the comprehensive properties of the alloy, the post-treatment of the SLM-formed TC4 titanium alloy is carried out using the hot isostatic pressing technology. The microstructure, micro-area elemental composition and surface microhardness of the alloys formed without post-treatment and by traditional heat treatment are compared. The results show that the microstructure of the alloy treated by hot isostatic pressing technology is composed of α phase and β phase, the number of pores is significantly reduced, the cracks are repaired, presenting an approximately granular structure, new Ti-Al intermetallic compounds and new phase Ti0.66Al3V0.33 are formed, and the Vickers hardness of the sample is 455.60 HV. The Vickers hardness of the alloy samples is increased by 40.9% and 18.1% respectively compared with those without post-treatment and those with traditional heat treatment. This provides a guarantee for its stable application in the manufacturing of automotive parts and offers technical support for improving the quality of titanium alloy components used in automobiles.

With the increasing application of composite materials in vehicle structural components, it is necessary to verify their vibration reliability. Considering the temperature sensitivity of resin matrix materials in composite materials, this study proposes a temperature based vibration testing method for vehicle structural components, which is more closely related to the actual environment of users and improves the accuracy of vibration fatigue testing for composite vehicle structural components. Taking the vibration fatigue test of composite material fender bracket as an example, the road load spectrum data of the fender bracket is first collected and preprocessed. Then, the ratio coefficient of empty and full load working conditions is introduced to calculate the acceleration random vibration spectrum of test bench. Then temperature based test bench vibration is carried out. Finally, the test results are evaluated using a combination of crack observation method, verticality detection method, and industrial CT three-dimensional scanning method, the test results are generally in line with the target specifications. The study demonstrates that the research findings can guide the implementation of vibration fatigue testing for composite vehicle structural components and facilitate the evaluation of test results.

In view of the high cost, long cycle and low autonomy caused by the dependence of integrators in the construction of automobile welding workshop, this paper puts forward a six-step self-reliance implementation path of "model establishment, joint definition, movement relationship configuration, three-dimensional layout construction, robot debugging, offline program test" based on process simulation technology. Through the process designer process simulate (PDPS) platform, taking the automation transformation project of the back cover of a welding workshop as an example, the system explains the whole process autonomous simulation method from equipment model configuration, simulation workstation construction, offline debugging and optimization to on-site online operation. Practice shows that this scheme can realize the accurate planning of the robot welding trajectory, the virtual prediction of collision risk and the efficient export of offline programs, effectively improve the autonomous control ability of enterprise process simulation technology, and provide an empirical basis for automobile manufacturing enterprises to promote technological self-reliance, reduce the cost of external cooperation, and reconstruct the competitiveness of the value chain.

This paper aims to explore the integration path of virtual simulation technology into the secondary vocational course Automotive Engine Mechanical System Inspection and Repair, so as to enhance students' vocational abilities and teaching effectiveness. Based on the concept of "virtualreal combination+task-driven approach+situational introduction," and taking into account the actual situation of a secondary vocational school in the western region, a virtual simulation teaching scheme centered on typical task units is designed. Through systematic teaching practice and task implementation, students demonstrated significant improvement in structural cognition, skill operation, and comprehensive abilities, effectively alleviating the problems of insufficient practical training resources and low teaching efficiency. The study shows that this teaching model achieves good results in promoting ability development, enhancing learning interest, and advancing the digital transformation of teaching. It provides a feasible path and practical reference for the digital reform of secondary vocational education and the promotion of educational equity.

In recent years, with the continuous upgrading of the new energy vehicle industry, the demand for high-quality skilled talents has been growing. Traditional teaching models are extremely inadequate in cultivating students' self-directed practical abilities, innovative capabilities, and job adaptability, leading to a persistent call for teaching reform. This article analyzes current issues from the perspectives of the school's regional environment, students' employment channels, students' overall competence, curriculum arrangement, and evaluation methods. Focusing on the teaching reform of specialized courses, it explores a teaching pathway that integrates multiple models, including the integration of theory and practice, online-offline blended learning,modularized curriculum and content, and differentiated instruction for students. Through practical application, combined with classroom evaluation results, the pass rate of practical skill assessments in specialized courses, and student satisfaction surveys, the approach has achieved significant outcomes. This study aims to provide important references for cultivating high-quality skilled talents who meet the development needs of the modern automotive industry, further improving the new energy vehicle inspection and maintenance technology course, and advancing teaching reform.

Generative artificial intelligence (AI), as an emerging branch in the AI field, has brought unprecedented challenges and new opportunities to higher education. Addressing the distinctive characteristics of academic disciplines and curriculum design, this study analyzes practical challenges in traditional teaching models while aligning with the talent development objectives of new engineering education. It proposes a teaching practice framework for Automotive Theory course reform through generative AI empowerment. By leveraging the DeepSeek model, the research explores effective integration of generative AI into instructional processes, continuously optimizing teaching objectives, course content, instructional activities, and evaluation systems. This approach ensures curriculum alignment with cutting-edge technologies, enhances teaching effectiveness, and elevates the quality of talent cultivation.

This paper delves into the innovative application of artificial intelligence in foundational professional courses. Taking the Fundamentals of Control Engineering course within the new energy vehicle engineering program as an example, we developes a learning data-driven intelligent teaching system. This system aims to enhance students' ability to solve practical vehicle engineering problems. The designed intelligent teaching system achieves deep integration of educational resources and multi-dimensional analysis of student learning data. It supports collaborative online-offline multimodal learning, real-time intelligent teaching evaluations, and the generation of personalized learning paths based on individual learning profiles. Through detailed case studies and empirical data, this research validates the effectiveness of the proposed solution in significantly boosting student learning interest, optimizing teaching outcomes, empowering autonomous student learning, and improving their ability to solve real-world engineering problems. This work offers a replicable and scalable paradigm for the intelligent teaching reform of other foundational professional courses.

In response to the current status of fostering character and civic virtue in vocational colleges, this article is committed to exploring an effective pathway for deeply integrating Party history education into the curriculum of higher vocational engineering majors, aiming to enhance the vocational key competencies and ideological and political literacy of higher vocational students. The research takes the multi-channel enrollment group of higher vocational engineering majors as the object and uses automotive professional courses as the carrier. Based on the stimulus-response theory, it organically incorporates case studies of Party history education for ideological and political cultivation into the curriculum, creating a synergistic effect and achieving vertical and horizontal integration between subject content and "Party history learning and education." Through practical verification, this method can add value and empower students, demonstrating favorable results in improving the quality of high-quality technical and skilled talent cultivation and possessing certain potential for wider application.

With the rapid development of manufacturing technology in China, commercial vehicles have achieved significant improvements in comfort and intelligence. At the same time, the electrical system of commercial vehicles also tends to be complicated, and the skill requirements of maintenance personnel are constantly improving, while the reserve of after-sales service and maintenance talents of commercial vehicles is seriously insufficient. The paper takes solving the shortage of commercial vehicle maintenance talents, the difficulty of recruiting maintenance personnel of commercial vehicle after-sales service stations, and the insufficient output of commercial vehicle professionals in higher vocational colleges as the starting point. From the three aspects of developing commercial vehicle maintenance core courses, practical training base planning and construction, and cooperation level evaluation, establish a three-way cooperation model of school-enterprise station training commercial vehicle maintenance talents, open a new mechatronic commercial vehicle talent training model, comprehensively and deeply promote the three-way cooperation of school-enterprise station, and take this opportunity to achieve win-win results. In the end, high standard talents for commercial vehicle after-sales maintenance channels are transported, which not only fulfills the social responsibility of the enterprise, but also ensures the employment rate of the school. To provide a formal and effective learning way for the training of commercial vehicle talents, sustainable and stable output of mature commercial vehicle talents for the society.

With the rapid development of the new energy vehicle industry, as a core component of the drive motor system, the training of its disassembly, assembly and maintenance skills is particularly important. However, training in the disassembly and maintenance of drive motor systems generally faces prominent issues such as high susceptibility to damage of precision parts, limited practical training space, difficulty in disassembling and assembling complex components, and restrictions on training scale. To address these challenges, this article takes the Volkswagen ID.4 CROZZ drive motor system as the research object. The three-dimensional model construction and optimization of the drive motor are completed through Blender, and the model is imported into the Unity3D platform to design and develop the virtual assembly system of the drive motor. The model is then imported into the Unity3D platform to design and develop a virtual assembly system for the drive motor. This system not only provides cognitive learning of component structures but also supports interactive manual disassembly and assembly operations of the motor drive system. It intuitively demonstrates the core structure and assembly process of the motor, significantly enhancing users' understanding and operational skills. This virtual system helps overcome the limitations of traditional training models in terms of resources, space and efficiency, providing innovative technical support for the cultivation of technical talents related to new energy vehicles.