Battery State-of-Health (SOH) Estimation

Go to Course: https://www.coursera.org/learn/battery-state-of-health

Introduction

### Course Review: Battery State-of-Health (SOH) Estimation on Coursera As technology continues to advance and the demand for sustainable solutions rises, the importance of battery technology is paramount, especially in the realm of electric vehicles and renewable energy storage. The Coursera course titled **Battery State-of-Health (SOH) Estimation** offers a comprehensive look into the methodologies and principles behind assessing battery health, particularly focusing on lithium-ion cells. This course is not only an excellent resource for professionals and students in the field but also serves as an academic credit course (ECEA 5733) at CU Boulder, as part of their Master of Science in Electrical Engineering degree. #### Course Overview The **Battery State-of-Health (SOH) Estimation** course equips participants with the knowledge and skills necessary to understand the degradation mechanisms of lithium-ion batteries and implement various state-of-health estimation methods. By the end of the course, learners will gain expertise in identifying degradation processes, executing estimation scripts in Octave/MATLAB, and evaluating different methods for estimating battery capacity. #### Key Learning Outcomes By engaging with this course, participants will be able to: - Identify the primary degradation mechanisms affecting lithium-ion cells. - Understand the physical and chemical processes behind these degradation phenomena. - Implement various estimation methods, including total-least-squares techniques, to evaluate battery capacity. #### Course Syllabus Breakdown 1. **How Does Lithium-Ion Cell Health Degrade?** - Participants explore the reasons behind capacity loss and resistance increase in aging battery cells. This foundational week sets the stage for understanding the challenges in accurately tracking total capacity, which is crucial for battery management systems (BMS). 2. **Total-Least-Squares Battery-Cell Capacity Estimation** - The course challenges the traditional ordinary-least-squares (OLS) methods, advocating for a total-least-squares (TLS) approach. Learners derive weighted OLS and TLS solutions, fostering a deeper understanding of statistical techniques applicable to battery evaluations. 3. **Simplified Total-Least-Squares Estimates** - Recognizing the challenges of computing weighted TLS efficiently on embedded systems, participants learn about the proportionally weighted TLS and approximate weighted TLS (AWTLS) methods that are both effective and computationally feasible for BMS applications. 4. **Coding for Total-Capacity Estimators** - This practical week emphasizes programming skills by guiding students in writing Octave code for various estimation methods. Real-world scenarios from hybrid and battery-electric vehicle applications are simulated to benchmark different methods, ensuring learners gain hands-on experience. 5. **Kalman-Filter Approach to Total Capacity Estimation** - Building on knowledge of extended Kalman filters (EKFs) and sigma-point Kalman filters (SPKFs), this week focuses on parameter estimation of battery-cell models, using advanced techniques that leverage known states for better accuracy. 6. **Capstone Project** - In the culmination of the course, participants undertake a capstone project that allows them to explore data utilization for total-capacity estimation methods. This project emphasizes the importance of data quality in achieving reliable battery health assessments. #### Why You Should Enroll The **Battery State-of-Health (SOH) Estimation** course is not only intellectually stimulating but also immensely relevant in today’s technology-driven landscape. Here are a few compelling reasons to enroll: - **Deep Dive into Battery Technology:** The course provides in-depth insights into one of the most critical aspects of battery technology—state-of-health estimation—making it invaluable for professionals working in energy, automotive, and battery management fields. - **Hands-On Learning:** With a strong emphasis on coding and practical applications, participants leave the course equipped with the skills to tackle real-world challenges in battery management. - **Academic Credibility:** For those pursuing higher education, the ability to earn academic credit through CU Boulder’s program adds value to your professional portfolio. - **Career Advancement:** Understanding battery SOH estimation positions you well for roles in clean energy, electric vehicles, and technology development—fields that are rapidly growing and evolving. In conclusion, **Battery State-of-Health (SOH) Estimation** on Coursera is a must-take course for anyone interested in battery technology. It combines theoretical knowledge with practical application, backed by solid academic rigor. Whether you’re looking to enhance your current skill set or delve into a new area of expertise, this course will provide you with the knowledge and tools necessary to excel in the fast-evolving world of battery technology.

Syllabus

How does lithium-ion cell health degrade?

As battery cells age, their total capacities generally decrease and their resistances generally increase. This week, you will learn WHY this happens. You will learn about the specific physical and chemical mechanisms that cause degradation to lithium-ion battery cells. You will also learn why it is relatively simple to estimate and track changes to resistance, but why it is difficult to track changes to total capacity accurately.

Total capacity is often estimated using ordinary-least-squares (OLS) methods. This week, you will learn that this is a fundamentally incorrect approach, and will learn that a total-least-squares (TLS) method should be used instead. You will learn how to derive a weighted OLS solution, to use as a benchmark, and how to derive a weighted TLS solution also.

Unfortunately, the weighted TLS solution you learned in week 2 is not well suited for efficient computation on an embedded system like a BMS. As an intermediate step toward finding an efficient weighted TLS method, you will first learn a proportionally weighted TLS method this week. You will then learn how to generalize this to an "approximate weighted TLS" (AWTLS) method, which gives good estimates, and is feasible to implement on a BMS.

So far this course, you have learned a number of methods for estimating total capacity. This week, you will learn how to implement those methods in Octave code. You will also explore different simulation scenarios to benchmark how well each method works, in comparison with the others. The scenarios are representative of hybrid-electric-vehicle (HEV) and battery-electric-vehicle (BEV) applications, but the principles learned can be extrapolated to other similar application domains.

In the third course of the specialization, you learned how to use extended Kalman filters (EKFs) and sigma-point Kalman filters (SPKFs) to estimate the state of a battery cell. In this honors week, you will learn how to extend those concepts to apply EKF and SPKF to estimating the parameters of a battery-cell model if the state is known, and also how to simultaneously estimate both the state and parameters of a cell model.

You have learned several different total-capacity estimation methods. Some of these methods work better than others in general, but any method is only as good as the data you give it. In this project, you will explore a different way to determine the "x" and "y" data you use as input to the total-capacity estimation methods.