Teaching reform and practice of combustion prediction and smart energy management

The fundamental operational nature of modern internal combustion engine (ICE) systems is intrinsically defined by an extremely strong degree of nonlinearity, as well as the intricate and multifaceted complex characteristics associated with high-dimensional interactions and multi-parameter coupling effects. The inherent physical complexities of the subject make it exceptionally difficult for traditional experimental teaching methodologies to successfully achieve high-precision combustion prediction or to realize the effective improvement of overall system energy efficiency in a consistent manner. One reason for this is that such methodologies often lack the necessary capacity for handling such massive amounts of multivariate data effectively. Within the specific context of contemporary engineering education, it has become imperative and critical to develop practical, technically effective, and systematic teaching frameworks. The overarching objective of these frameworks is to systematically foster and cultivate students’ essential capabilities in the fields of computational modeling and strategic decision-making. This enables them to successfully navigate the multifaceted challenges involved in dealing with the modeling and optimization of the complex engineering problems encountered in real-world scenarios.

This teaching reform is a direct and targeted response to identified pedagogical and technical challenges. It proposes an innovative, comprehensive, and integrated method specifically designed for the purposes of ICE combustion prediction and smart energy management. This method integrates advanced machine learning technologies into the existing curriculum in a seamless and deep way. The methodological framework is meticulously implemented through a rigorous, structured, and sequential process. First, a systematic sampling design strategy is meticulously executed for the specific test content to ensure comprehensive data coverage of the operating space. The extensive datasets obtained from these comprehensive experimental tests are then utilized as the foundational material to train sophisticated machine learning models. Following this preliminary data acquisition phase, a robust and high-fidelity ensemble tree surrogate model is computationally constructed to achieve high-precision fitting and accurate prediction of critical ICE performance indicators. This surrogate model serves as a reliable digital proxy for the physical engine system. Finally, advanced multi-objective optimization algorithms are integrated into the workflow to process these predictive models, with the aim of generating a Pareto optimal solution set that represents the ideal mathematical balance between conflicting operational goals.

The results of the quantitative optimization derived from the implementation of the present study provide compelling and robust empirical evidence that demonstrates the practical effectiveness of the framework. The experimental data unequivocally demonstrate that, under the stipulated trade-off operating condition, ICE’s fuel consumption is significantly reduced by a substantial margin of 14.5%. Concurrently, the environmental performance has been notably enhanced, with exhaust emissions of hydrocarbons (HC), carbon monoxide (CO), and nitrogen oxides (NOx) reduced by 7.0%, 5.7%, and 32.8%, respectively. This outcome validates the efficacy of the proposed optimization strategy.

In summary, the test teaching reform framework was successful in establishing a complete, comprehensive, and logical closed loop encompassing the iterative stages of “experimental design–data modeling–optimization feedback.” This structured pedagogical approach enables students to have a solid foundation in the scientific research process, extending seamlessly from the initial phases of test design to the final and critical stages of data analysis and parameter optimization. Moreover, it considerably augments their comprehensive capacity to solve complex scientific problems by efficaciously bridging the divide between theoretical academic knowledge and practical engineering application. This teaching reform offers a valuable, replicable, and promotable paradigm for the future teaching reform practice in energy and power-related majors.