Session: 06-04: Performance Systems Modeling and Design

Paper Number: 164375

164375 - Application of Model-Based Systems Engineering Methods to Advanced Energy Locomotive Design and Optimization 

Abstract: 

A conventional diesel-electric locomotive is a highly complex system integrating subsystems from multiple domains, including electrical, mechanical, electromechanical, power electronics, and control systems. Designing and developing such machines has traditionally been time-consuming and resource-intensive due to intricate interdependencies among these domains. As the rail industry transitions toward advanced energy locomotive technologies, reducing development cycles and improving design efficiency become critical.

Model-Based Design (MBD) offers a powerful approach to accelerating this transition by using mathematical models to simulate, analyze, and validate system behavior before building physical prototypes. By leveraging MBD, engineers can streamline development, optimize system performance, and efficiently explore design alternatives. Integrating system-level models and refining model development processes further expedite design iterations, enabling the rapid evaluation of new locomotive technologies. Instead of relying on traditional hand-coded software development, engineers can use graphical models to design, analyze, and implement control and performance algorithms, reducing complexity while improving accuracy and adaptability.

This paper introduces Autonomie Rail, a novel rail simulation tool designed to support the development and evaluation of advanced energy locomotives in an emulated environment. Built upon Autonomie, Argonne National Laboratory’s established plug-and-play simulation framework for automotive applications, Autonomie Rail extends these capabilities to meet the evolving technological needs of the rail industry. The tool provides a flexible and modular simulation environment, enabling engineers to model various locomotive architectures—including battery-electric, diesel-electric battery hybrid, and hydrogen fuel cell systems—for both passenger and freight applications.

Autonomie Rail offers a fully integrated environment with structured processes for managing, interconnecting, and integrating dynamic models of locomotive components and subsystems. Models are developed using MATLAB, Simulink, and Stateflow, allowing for detailed and dynamic system simulations. Additionally, a graphical user interface written in C# enhances user interaction, making it easier for engineers to configure simulations and analyze results efficiently. This enables comprehensive system-level simulations, supporting a range of analyses from propulsion system trade-offs to detailed control system design. Its plug-and-play architecture allows users to incorporate component and subsystem models of varying fidelity, facilitating targeted investigations into specific areas of interest. Engineers can rapidly evaluate different design approaches, optimize energy management strategies, and refine control algorithms without the time and cost constraints of physical prototyping.

This paper presents best practices for leveraging Autonomie Rail in locomotive design and demonstrates its capabilities through the evaluation of battery-electric and hydrogen fuel cell locomotives. By integrating advanced simulation tools such as Autonomie Rail into the rail industry’s design workflow, engineers can significantly reduce development timelines, enhance system efficiency, and accelerate the deployment of next-generation energy-efficient locomotives.

Presenting Author: George Burke Argonne National Laboratory

Presenting Author Biography: George Burke is a researcher at Argonne National Laboratory, specializing in rail modeling, simulation, and advanced locomotive technologies. With over 12 years of experience in mechanical engineering, he focuses on developing Model-Based Design (MBD) approaches to accelerate the development of next-generation rail propulsion systems. At Argonne, George plays a key role in advancing Autonomie Rail, a simulation tool for evaluating advanced energy locomotives. His expertise in multidomain system modeling, control algorithms, and energy management helps optimize rail system performance and efficiency. Before joining Argonne, George gained valuable experience at leading organizations such as Ingersoll Rand, and Volvo Group. In these roles, his work was primarily centered on powertrain system modeling and conducting comprehensive performance evaluations.

Authors:

George Burke Argonne National Laboratory
Ram Vijayagopal Argonne National Laboratory
Namdoo Kim Argonne National Laboratory
Phillip Sharer Argonne National Laboratory
Aymeric Rousseau Argonne National Laboratory