Under the background of the New Energy Vehicle Industry Development Plan, higher education faces the critical task of cultivating high-level interdisciplinary engineering talents. Power batteries are the core components of new energy vehicles, which involve complex electrochemical–thermal coupling mechanisms. However, traditional teaching practices encounter several challenges: the abstract nature of electrochemical models makes it difficult for students to understand their internal mechanisms; safety risks and equipment limitations restrict high-rate charging and discharging experiments in the classroom; and the fragmented teaching of modeling, parameter identification, and experimental verification lacks a systematic closed-loop perspective. Therefore, this study aims to develop a reproducible and integrated multiphysics simulation teaching case. By focusing on Blade batteries, the research explores an integrated design of mechanism modeling, cosimulation, and parameter identification, aiming to transform abstract theoretical models into intuitive cognitive experiences and improve students' abilities to solve complex engineering problems.

This study implements a simulation teaching scheme based on the synergy of mechanism models and surrogate acceleration. First, a multiphysics coupling model of a commercial Blade battery is established. The electrochemical behavior is characterized by a pseudo-two-dimensional model based on the Doyle–Fuller–Newman framework, describing lithium-ion distribution and dynamics. This is bidirectionally coupled with a three-dimensional solid heat transfer model in COMSOL Multiphysics, where electrochemical heat sources (including reversible entropy heat, ohmic heat, and polarization heat) drive temperature evolution, while the temperature field simultaneously adjusts the electrochemical parameters such as diffusion coefficients and exchange current densities. Second, a coordinated MATLAB–COMSOL simulation environment is developed for parameter identification. To overcome the high computational cost of high-fidelity simulations, a surrogate-assisted Teaching–Learning-Based Optimization (TLBO) framework is introduced. The identification process is decoupled into two sequential stages: the first stage identifies 21 electrochemical parameters using the terminal voltage response, and the second stage identifies 8 thermal parameters based on multipoint temperature data. A Kriging surrogate model is constructed to approximate the expensive objective function, and a lower confidence bound acquisition function is employed to balance exploration and exploitation. This strategy triggers high-fidelity simulation feedback only for the most promising candidates, significantly reducing the number of model calls while maintaining high identification accuracy.

The simulation teaching case was validated through experimental data collected from a high-precision battery test platform. Under the 1C constant-current discharge condition, the simulation terminal voltage curves were highly consistent with the experimental measurements, accurately reproducing the local fluctuation characteristics of the discharge platform. The identified electrochemical parameters yielded a root mean square error (RMSE) of 0.046699 V. Regarding the temperature field, the model successfully reconstructed the spatial temperature distribution, showing that the high-temperature region was concentrated near the positive tab and diffused toward the center. The average temperature RMSE remained at 1.021926 K. To evaluate the generalization capability, the identified parameters were extrapolated to a 1.5C discharge condition without recalibration. The model accurately captured the downward shift of the voltage platform and the accelerated temperature rise caused by the increased rate, maintaining a high determination coefficient. Crucially, compared to the traditional optimization method without surrogate assistance, the proposed strategy reduced the number of high-fidelity model calls from 9600 to 349, achieving a 96.4% improvement in computational efficiency. Furthermore, sensitivity analysis revealed that voltage prediction is dominated by geometric and mass transfer parameters, while temperature prediction is primarily governed by boundary convection and thermal conductivities.

This research provides a systematic and efficient pathway for the teaching of power battery simulation. By integrating mechanism modeling with surrogate-assisted optimization, the proposed teaching mode effectively resolves the conflict between simulation accuracy and computational time in the classroom setting. The visualization of multiphysics fields helps students bridge the gap between abstract mathematical equations and physical phenomena. The quantitative sensitivity analysis and cross-rate validation further cultivate students' rigorous engineering thinking and abilities to interpret complex system behaviors. This case study serves as a valuable reference for the construction of virtual simulation laboratories and the reform of courses related to new energy vehicle engineering.

The Smart Factory curriculum integrates disciplines such as computer science, automation, and information technology, emphasizing hands-on practice and skill development. However, traditional teaching methods often focus on theoretical knowledge regarding interactivity and participation and fail to promptly incorporate teaching content related to new technologies and equipment. This course develops a practical teaching strategy that encourages students to participate in factory demand research. By simulating real factory environments and production processes, this method introduces the latest technological achievements, namely the multi-agent reinforcement learning task supervisor, into smart warehouse system case studies, thereby enhancing students’ skills and innovation.

To address challenges in path planning and real-time delivery sequence adjustment for multi-load warehouse robots operating in dynamic environments, we propose a comprehensive policy that integrates local path planning, dynamic obstacle avoidance, and real-time delivery cost evaluation. This paper models a warehouse environment and captures its layout, shelf positions, aisle widths, and potential obstacle locations to frame multi-objective delivery as a traveling salesman problem. To ensure safe navigation, the robot tasks were divided into movement, obstacle avoidance, and collision avoidance. In complex warehouse environments, robots frequently execute multiple behaviors concurrently. We introduce a null-space behavioral control algorithm to manage conflicts, where behaviors are projected into null spaces based on their assigned priorities to form composite behaviors. Priorities are determined by a multi-agent reinforcement learning task supervisor, who uses composite behavior velocities as actions for the deep reinforcement learning algorithm and the robot positions as states. Through continuous interaction with the environment during the learning process and offline training, the supervisor develops a priority selection policy. Furthermore, the delivery sequence for warehouse robots is dynamically adjusted using a scoring evaluation mechanism. This mechanism updates the selection of target delivery points in real time to minimize transportation costs. This policy ensures that each warehouse robot safely delivers multiple items over the shortest possible distance while avoiding collisions, thereby providing an optimal solution for commodity delivery in warehouse systems.

This simulation compared the effectiveness of a reinforcement learning task supervisor to a multi-agent reinforcement version in a warehouse environment. Although warehouse robots equipped with a reinforcement learning task supervisor can navigate paths and avoid obstacles, frequent priority switches result in longer paths. By contrast, warehouse robots utilizing a multi-agent reinforcement learning task supervisor, enhanced by a scoring evaluation mechanism, can intelligently adjust the delivery sequence based on path length costs and optimize path selection through goal-oriented learning. This approach not only reduces transportation path lengths but also effectively shortens dynamic obstacle avoidance distances and waiting times due to refined obstacle and collision avoidance behavior designs. More stable priority switching significantly enhances delivery flexibility, decreases path lengths, and alleviates traffic congestion.

Considering the course characteristics and teaching challenges of smart factories, this paper presents a simulation teaching scheme for multi-warehouse robot path planning tailored to smart factories. It addresses challenges in multi-objective delivery and multi-task conflicts by using a scoring evaluation mechanism to dynamically update delivery sequences of target points, optimize robot transportation paths, and improve the overall efficiency of warehouse system operations. This teaching scheme leverages Python software platforms for model experiments, enhances practical teaching resources, and employs virtual simulations to compensate for hardware deficiencies. By evaluating different solutions, students gain a deeper understanding of smart factory requirements and enhance their problem-solving skills.

The course content for unmanned aerial vehicle (UAV) control technology is extensive and complex, encompassing a broad spectrum of theoretical knowledge from various disciplines. It involves strong mathematical logic relationships and many formula derivations. The traditional teaching method, however, has proven to be inadequate in cultivating students’ innovative and critical thinking abilities, resulting in disappointing academic results. Therefore, the challenge and focus of this course lie in discovering an effective teaching strategy. One that encourages each student’s active participation and provides them with opportunities to demonstrate their understanding and skills. To cultivate the students’ problem-solving abilities within the context of UAV control courses, it is imperative to stimulate their creative thinking. This can be achieved through research guidance on course design and by implementing simulation teaching research on UAV formation energy consumption optimization.

The concept of multidrone formation switching presents unique challenges. Existing solutions, such as the Hungarian algorithm, can solve the assignment problem of the optimal total switching distance assignment. However, these solutions often result in certain UAVs being assigned excessively long flight paths or individual UAVs being tasked with high climbs. This invariably leads to higher power consumption during flight than during hovering, causing a rapid decrease in power consumption during formation switching. The outcome is a shorter flight time for the entire formation compared to other UAVs. To this end, we have refined the allocation plan. Our goal is to ensure that the flight paths of the UAVs during the formation switching process are similar and the flight times to the target waypoints are consistent, thereby avoiding the above problems. In this context, we employ the particle swarm optimization (PSO) algorithm to design the flight distance and climbing distance costs for the UAVs. We set an appropriate objective function and solve the linear programming in each iteration to find the optimal assignment solution for the current position of each particle. The fitness of the partic le swarm in the iterative process is calculated, and the optimal solution is obtained by comparing the fitness of the particle swarm. After determining the optimal solution for UAV cluster formation switching, it is also necessary to ensure that each UAV reaches its respective target point safely, without collision. Therefore, during the formation switching, the task for each UAV can be decomposed into two subtasks: moving to the target point and avoiding collisions. However, this process can lead to multitask conflicts owing to potential collisions between UAVs. To effectively manage these conflicts, we introduce the null space behavior control algorithm. This algorithm ensures that each UAV avoids collisions while navigating to the target point, thus providing a comprehensive resolution for multitask conflicts.

Experimental results from simulations indicate that under the traditional particle swarm algorithm, half of the UAVs remain static during multi-UAV formation. This leads to a significant disparity in energy consumption among the UAVs, with some consuming excessive energy. This phenomenon, known as the “barrel effect,” greatly diminishes the task execution efficiency of UAV clusters. By introducing an optimized particle swarm algorithm that balances overall flight energy consumption, we can mitigate these issues. Under this optimization, the entire UAV formation will shift slightly, and multiple UAVs will prioritize target points reachable through descending motion. In addition, the importance of safety measures during task execution is evident when comparing different behavioral control frameworks. Without collision avoidance tasks, the distances between drones exceed the safe limit, jeopardizing task execution safety. However, when collision avoidance tasks are implemented within the behavioral control framework, the drones maintain a safe distance from each other. This ensures safety in dynamic formation switching and achieves the goal of preventing collisions between UAVs.

Considering the unique characteristics and teaching needs of UAV control technology courses, we have conducted simulation teaching research. This approach specifically addresses the energy optimization issue in UAV formation switching. We have designed a solution for the linear assignment problem and improved the objective function of the PSO algorithm. These enhancements effectively address the energy consumption challenge during UAV formation switching, thereby extending the flight time of the formation. Additionally, we have employed a behavioral control method to ensure the simultaneous arrival of the UAV cluster at the destination. This method, which includes reasonable task output based on each UAV’s path in the planned formation reconstruction, also enables collision avoidance. MATLAB numerical simulations were performed to compare the performance of different solutions. This design is incorporated into the teaching of graduate-level UAV control technology courses. By integrating theoretical analysis with engineering application experiments, we stimulate innovative thinking among students and heighten their interest in subject learning.