Battery Pack Balancing and Power Estimation

Go to Course: https://www.coursera.org/learn/battery-pack-balancing-power-estimation

Introduction

### Course Review: Battery Pack Balancing and Power Estimation on Coursera The course titled **Battery Pack Balancing and Power Estimation** is a well-structured program available on Coursera, tailored for both enthusiasts and professionals interested in battery management systems (BMS). This course can also be credited toward the Master of Science in Electrical Engineering at CU Boulder, making it an excellent option for those pursuing advanced academic credentials while enhancing their technical skills. #### Course Overview: In today's world where electronic devices heavily rely on effective battery management, understanding how to maintain battery health and efficiency is paramount. This course dives deep into the nuances of battery balancing systems and energy estimation, encompassing both passive and active methods. Over six weeks, participants will engage in practical learning, culminating in a capstone project that incorporates theoretical knowledge gained throughout the course. ##### Learning Objectives: By the end of the course, participants will be proficient in: - Evaluating various design choices for cell balancing and articulating their respective strengths and weaknesses. - Designing component values for a simple passive balancing circuit. - Implementing algorithms using Octave for battery management tasks such as balancing and energy estimation. #### Syllabus Highlights: 1. **Passive Balancing Methods:** The course begins with an introduction to passive balancing methods, highlighting the natural imbalance of battery packs and exploring effective strategies for correction. The initial lessons lay a solid foundation for understanding passive circuits used in balancing. 2. **Active Balancing Methods:** Building upon the knowledge of passive methods, the course transitions into active balancing techniques that aim to conserve energy. Participants will learn to write Octave code that assesses battery imbalance dynamics, a crucial skill for maintaining battery performance. 3. **Available Battery Power using Simplified Cell Model:** Understanding the limits of battery power is critical. This section reviews the Hybrid Pulse Power Characterization (HPPC) method, allowing learners to estimate power limits while accounting for state of charge and other factors. 4. **Comprehensive Cell Model for Power Limits:** In this advanced section, students will explore a more rigorous analysis using full-state information. The module provides insights into modeling that aligns closer to real-world battery behaviors, enhancing the accuracy of power limit estimations. 5. **Future BMS Algorithms:** The forward-looking segment introduces concepts for next-generation BMS algorithms, focusing on physics-based models that could provide deeper insights into internal cell processes, potentially leading to advancements in preventing cell degradation. 6. **Capstone Project:** The capstone project offers a hands-on opportunity to design a resistor value for a switched-resistor passive balancing system and enhances the understanding of power-limits methodologies, solidifying the course's practical aspects. #### Review and Recommendation: The *Battery Pack Balancing and Power Estimation* course is a commendable addition to Coursera's collection, particularly for those working or interested in the field of electric vehicles, renewable energy storage, or any domain where battery systems play a critical role. It is particularly valuable for engineers, researchers, and graduate students aiming to deepen their understanding of battery management systems. **Pros:** - Comprehensive coverage of both theoretical and practical insights. - Engaging curriculum with real-world applications. - Access to Octave coding, enhancing coding skills relevant to battery systems. - Academic credit option, providing a pathway for further education. **Cons:** - Requires a foundational understanding of electrical engineering concepts, which might be challenging for complete beginners. In conclusion, I highly recommend this course to anyone eager to expand their knowledge in battery technology and management systems. It not only equips learners with vital skills for immediate application but also lays a groundwork for future innovations in the energy sector. Dive into this course for a transformative learning experience that merges theory with practical, impactful applications.

Syllabus

Passive balancing methods for battery packs

In previous courses, you learned how to write algorithms to satisfy the estimation requirements of a battery management system. Now, you will learn how to write algorithms for two primary control tasks: balancing and power-limits computations. This week, you will learn why battery packs naturally become unbalanced, some balancing strategies, and how passive circuits can be used to balance battery packs.

Passive balancing can be effective, but wastes energy. Active balancing methods attempt to conserve energy and have other advantages as well. This week, you will learn about active-balancing circuitry and methods, and will learn how to write Octave code to determine how quickly a battery pack can become out of balance. This is useful for determining the dominant factors leading to imbalance, and for estimating how quickly the pack must be balanced to maintain it in proper operational condition.

This week, we begin by reviewing the HPPC power-limit method from course 1. Then, you will learn how to extend the method to satisfy limits on SOC, load power, and electronics current. You will learn how to implement the power-limits computation methods in Octave code, and will see results for a representative scenario.

The HPPC method, even as extended last week, makes some simplifying assumptions that are not met in practice. This week, we explore a more accurate method that uses full state information from an xKF as its input, along with a full ESC cell model to find power limits. You will learn how to implement this method in Octave code and will compare its computations to those from the HPPC method you learned about last week.

Present-day BMS algorithms primarily use equivalent-circuit models as a basis for estimating state-of-charge, state-of-health, power limits, and so forth. These models are not able to describe directly the physical processes internal to the cell. But, it is exactly these processes that are precursors to cell degradation and failure. This week quickly introduces some concepts that might motivate future BMS algorithms that use physics-based models instead.

This capstone project explores the design of resistor value for a switched-resistor passive balancing system as well as enhancing a power-limits method based on the HPPC approach.