Engine combustion modeling method based on hybrid drive.

This project presents a step‑by‑step framework for modeling internal‑combustion‑engine behavior with a hybrid‑drive approach. Conventional physical models are easy to interpret, but they can be slow and lose accuracy when the engine operates under rapid, changing conditions. Purely data‑driven models avoid those speed issues, yet they ignore the underlying physics and can give unrealistic results when asked to predict outside the data they were trained on. To overcome these problems, the project shows how to build a coupled model that combines zero‑dimensional thermodynamic conservation equations with machine‑learning algorithms. The physics part enforces basic mass‑ and energy‑balance rules, while the machine‑learning part learns the complex, non‑linear aspects of combustion such as

turbulent flame spread, detailed chemical reactions, and cycle‑to‑cycle variations. The hybrid model is created and tested using MATLAB/Simulink together with Python‑based neural‑network libraries. It is trained on, and validated against, representative engine data sets. The main performance targets are cylinder‑pressure histories, apparent heat‑release rates, and exhaust‑emission levels, evaluated over a wide range of engine speeds and loads. The guide is written for M.Tech students. It walks them through building, checking, and analyzing the hybrid simulation, giving a solid base for later work on power‑train control and optimization.

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