EN3180 Strategic Battery Acquisition and Modeling for Resilience in Defense Applications

The first half of this course provides a comprehensive review of battery technologies and acquisition strategies critical to defense resilience. Students will examine the strategic importance of batteries in military operations, current procurement processes, and challenges related to affordability, security, and integration. The course explores mitigation strategies for foreign supply dependence and supply chain risks and introduces performance management tools and decision-making frameworks.

In the second half, students will be introduced to the application of Model-Based Systems Engineering (MBSE) within the domain of advanced battery systems, while grounding the learning experience in strong system engineering foundations. The course will begin with an overview of core systems engineering principles, terminology, lifecycle concepts, and systems thinking to establish a common framework for approaching complex engineering problems.

Building on this foundation, students will progress into the practical application of MBSE, including exposure to established MBSE tools, languages, and methodologies. Students will learn how modeling techniques can be used to define, analyze, and validate battery system architectures across the full life cycle – from cell chemistry and subsystem design to through pack integration, operational deployment, sustainment, and end-of-life considerations.

Modeling approaches such as SysML, CAMEO, and other domain-relevant methods are used to represent system requirements, behaviors, structures, interfaces, and performance characteristics. This course will also address multiple-domain integration (electrical, thermal, mechanical), safety and regulatory considerations, and the use of digital threat concepts to support lifecycle traceability. This course will include a MBSE project, in which students develop a complete system model of a battery-based system, applying the concepts, tools, and methodologies learned through the course to manage complexity, assess design tradeoffs, and support engineering decision-making. The course will conclude with exploration of advanced and emerging topics in MBSE, including AI integration, automation, intelligent engineering systems, digital engineering evolution, and future directions of MBSE practices, giving students insight into how MBSE is evolving and how next-generation tools and techniques may further transform system engineering.

Cross Listed Courses

None

Prerequisite

Introductory coursework in systems engineering or energy storage systems is recommended. Experience with modeling tools or simulation platforms is beneficial but not required.

Corequisite

None

Lecture Hours

4

Lab Hours

0

Course Learning Outcomes

CLO 1 – Evaluate Acquisition Challenges and Supply Chain Risks in Military Battery Systems

Students will identify and explain major acquisition challenges in military battery systems, including fragmented requirements, lack of standardization, limited domestic suppliers, foreign supply chain vulnerabilities, and evolving mission needs, using examples from DoW case studies and stakeholder analyses. Students will assess the national security implications of these challenges, identifying risks and recommending actionable interventions.

CLO 2 – Apply Risk Management Tools to Propose Solutions in Battery Acquisition Scenarios

Given a hypothetical military battery acquisition case, students will apply at least one supply chain or acquisition risk assessment framework (e.g., risk matrix, supplier vetting tool) to identify procurement vulnerabilities and propose feasible, evidence based solutions that address stakeholder needs, acquisition constraints, and supply chain risks.

CLO 3 - Explanation of core systems engineering principles and MBSE concepts

Understand systems engineering terminology, lifecycle models, and system thinking. Differentiate between traditional document-centric approaches and Model-Based Systems Engineering (MBSE), including historical drivers and modern digital engineering initiatives.

CLO 4 - Apply MBSE methodologies across the system lifecycle

Use MBSE to support requirement development, architecture definition, design analysis, verification, validation, sustainment, and end-of-life planning. Represent system requirements, structure, behavior, interfaces, and performance characteristics using modeling approaches.

CLO 5 - Demonstrate proficiency in SysML and MBSE tools to model and analyze complex systems

Develop foundational skills in SysML constructs for structural and behavioral modeling, requirements, parametric diagrams, and integration. Use MBSE tools to create consistent and traceable system models, applying them to assess supply chain risks, identify critical dependencies, and propose mitigation strategies. Leverage traceability techniques to support engineering trade studies, acquisition decisions, and lifecycle strategies using model-supported performance metrics.

CLO 6 – Evaluate and apply emerging MBSE directions

Analyze and evaluate emerging Model‑Based Systems Engineering (MBSE) directions by assessing AI‑enabled modeling techniques, automation tools, and intelligent engineering system capabilities, and produce an analysis demonstrating how these technologies can enhance MBSE workflows.