1. Knowledge and Understanding Understand the role and key functions of Battery Management Systems (BMS) in safety, performance, and energy management. Acquire knowledge of battery pack modeling techniques, state estimation principles (SOC, SOE, SOH), cell degradation, and balancing strategies. Comprehend the application of model-based optimal control methods to battery systems, with a focus on lithium-ion technology. 2. Applying Knowledge and Understanding Simulate battery behavior using equivalent models and validate system responses under different load and aging conditions. Design and implement estimation algorithms (e.g., Kalman filters) and optimal controllers for energy management using Matlab or Python. Apply battery models and control techniques in practical scenarios such as electric vehicles and embedded systems. 3. Making Judgments – Evaluation and Assessment Evaluate the suitability of different estimation and control strategies for specific applications. Assess trade-offs between performance, safety, and longevity in battery system design and management. Interpret data and simulation results critically to identify limitations and suggest improvements. 4. Communication Skills – Effective Communication Clearly explain the principles and implementation of BMS algorithms, both in written reports and oral presentations. Communicate technical concepts effectively to both specialized and non-specialized audiences. 5. Learning Skills – Research and Exploration Develop the ability to autonomously explore scientific literature and industrial documentation on battery management and state estimation. Integrate new knowledge into engineering practice to propose innovative solutions to real-world energy storage problems.

Basic knowledge in the following areas: -Electrical circuits and electrotechnics - Dynamical systems and automatic control - Basic programming skills in Matlab or Python

This course provides a comprehensive overview of the key aspects of rechargeable battery management, with a focus on applications in electric vehicles and advanced energy systems. Students will develop the skills needed to address real-world challenges related to efficiency, reliability, and safety in battery-powered systems. 1. Requirements and Functions of Battery Management Systems (BMS) General introduction to the role and objectives of battery management systems, including safety, monitoring, and energy management within application-driven contexts. 2. Battery Pack Modeling Development and use of models to predict battery behavior under various operating conditions, with a focus on performance and driving range. 3. Battery State Estimation Techniques for estimating key internal quantities such as state of charge and energy, essential for safe and efficient battery operation. 4. Degradation and State of Health (SOH) Analysis of battery aging phenomena and strategies to monitor and assess the health status of the system over time. 5. Cell Balancing Understanding why imbalance occurs between cells and how it can be corrected to improve performance and extend battery life. 6. Model-Based Optimal Control Model predictive strategies for smart energy management, designed to optimize system efficiency and longevity while respecting performance and safety constraints.

G.L. Plett, Battery Management Systems, Vol. I: Battery Modeling, Vol. II: Equivalent-Circuit Methods, Vol. III: Battery State Estimation, Artech House. Instructor’s lecture notes and selected scientific articles provided throughout the course. Software tools and simulation scripts made available in Matlab and/or Python. In general, the lecture notes and materials provided by the instructor are sufficient for exam preparation.

The course combines lectures, guided exercises, and hands-on laboratory activities, with approximately 35% of the total time dedicated to lab work. Lectures provide the theoretical foundation and context for addressing real-world battery management challenges. Exercises guide students through the design and analysis of estimation and control algorithms for battery packs. Laboratory sessions—conducted using programming environments such as Matlab or Python—enable students to implement, test, and validate their solutions, reinforcing learning through practical experimentation and application.

Student assessment will, unless otherwise specified, consist of a written final exam aimed at evaluating the understanding of key concepts and the ability to apply them to practical scenarios. Alternatively or in addition, a project-based assessment may be proposed, to be completed individually or in small groups. Final details regarding the exam format (written, project-based, or combined) will be provided at the beginning of the course.

This course explores topics closely related to one or more goals of the United Nations 2030 Agenda for Sustainable Development (SDGs)