Futuristic engine surrounded by data streams and circuit patterns.

Unlock Engine Efficiency: A Guide to System-Level Simulation Models

Soraya Malik in Tech & Innovation June 2025 • 4 min read.

"Optimize Performance and Predict Behavior with Advanced Engine Simulation Techniques"

In an era defined by sustainable energy and environmental consciousness, the automotive industry is undergoing a profound transformation. Modern engines, designed to meet stringent environmental standards, integrate a diverse array of technologies. From advanced turbocharging systems that minimize fuel consumption to sophisticated exhaust aftertreatment measures that optimize pollutant formation, today's engines are marvels of engineering complexity.

Amidst this complexity, computer simulation has emerged as an indispensable tool in the power train development process. System-level simulations, in particular, are crucial for integrating various domains such as the engine, drive train, cooling, exhaust aftertreatment, and control systems in a multi-physical manner. These simulations provide valuable insights during the early concept phase, where measurements are limited and models must rely on first-order principles and empirical approaches.

However, the effectiveness of system-level simulations hinges on the accuracy and consistency of the engine models employed. Traditional approaches often involve developing separate models for the concept and calibration phases, leading to inconsistencies and duplicated effort. A more streamlined approach is needed to ensure horizontal consistency across all development stages.

Decoding System-Level Engine Simulation Models

Futuristic engine surrounded by data streams and circuit patterns.

System-level engine simulation involves using computer models of varying complexity to predict engine performance and behavior. These models range from comprehensive, transient 1D cycle-resolved approaches to simpler, steady-state non-dimensional maps. Understanding the key features, data requirements, calibration effort, and predictability of each model is essential for selecting the right tool for the job.

The study categorizes engine models based on their depth and computational expense:

1D Gas-Dynamic Models: These models offer a high level of fidelity, capturing transient gas dynamics in detail, but can be computationally intensive.
0D Gas Dynamic Models: These models simplify gas dynamics, reducing computational cost while still capturing essential phenomena.
Mean Value Models: These models focus on average values, providing a computationally efficient approach for steady-state simulations.
Surrogate Models: These models use data-driven approaches to emulate engine behavior, offering real-time capabilities for control design and calibration.
Steady-State Map Based Engine Models: This involves the application of steady state engine maps can be seen as the simplest approach to describe the entire engine in a data driven manner.

In the trade-off between model predictability and computational performance, an innovative approach to crank-angle resolved cylinder modeling has proven particularly beneficial. This approach combines the high level of physical-based approaches needed in early development phases with the real-time capabilities required for calibration in later phases.

The Future of Engine Design: Embracing System-Level Simulation

As the automotive industry continues to evolve, system-level engine simulation models will play an increasingly vital role in optimizing engine design, improving fuel efficiency, and reducing emissions. By carefully selecting the appropriate models and ensuring consistency across all development stages, engineers can unlock new levels of performance and sustainability.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.4271/2013-01-1116, Alternate LINK

Title: A Comprehensive Study On Different System Level Engine Simulation Models

Journal: SAE Technical Paper Series

Publisher: SAE International

Authors: Johann C. Wurzenberger, Roman Heinzle, Maxime-Vianney Deregnaucourt, Tomaz Katrasnik

Published: 2013-04-08

Everything You Need To Know

Why is system-level engine simulation important in powertrain development?

System-level engine simulation is important because it integrates different areas like the engine, drive train, cooling, exhaust aftertreatment, and control systems. By using these simulations, engineers can gain important information early in the design phase. At this stage, measurements are limited, so models depend on basic principles and real-world testing to inform decisions.

Can you explain the differences between the different types of system-level engine simulation models, such as 1D Gas-Dynamic Models, 0D Gas Dynamic Models, Mean Value Models, Surrogate Models and Steady-State Map Based Engine Models?

1D Gas-Dynamic Models are very detailed and can show quick changes in gas behavior, but they need a lot of computing power. 0D Gas Dynamic Models simplify how gas moves to lower computing costs while capturing the most important details. Mean Value Models focus on average values to make steady-state simulations faster. Surrogate Models use data to copy how an engine acts, giving real-time performance for control design and fine-tuning. Steady-State Map Based Engine Models involves the application of steady state engine maps can be seen as the simplest approach to describe the entire engine in a data driven manner.

What is the key to making system-level simulations effective, and what problems can arise from traditional modeling approaches?

The key to effective system-level simulations lies in using engine models that are both accurate and consistent. Traditional methods often create different models for initial design and later calibration, leading to discrepancies and wasted effort. A better method involves making sure there's consistency across all development stages to avoid these issues.

In what specific ways can system-level engine simulation models improve engine design, fuel efficiency, and reduce emissions?

System-level engine simulation models improve engine design by allowing engineers to test and optimize different designs using computer models, leading to better performance and reduced emissions. These simulations help to improve fuel efficiency by finding ways to minimize fuel consumption through the adjustment of engine parameters and configurations. They help to reduce emissions by optimizing exhaust aftertreatment processes and lowering pollutant formation. The models help in selecting appropriate models and ensuring consistency across all development stages.

What is the benefit of crank-angle resolved cylinder modeling approach in balancing predictability and computational performance?

Crank-angle resolved cylinder modeling combines physically-based approaches needed in early development phases with real-time capabilities required for calibration in later phases. This balance is crucial as it allows for accurate predictions and adjustments during the entire development process, from initial design to final calibration.