Battery State-of-Charge (SOC) Estimation

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

Introduction

### Course Review: Battery State-of-Charge (SOC) Estimation on Coursera **Overview** In the rapidly evolving field of battery technology, understanding the mechanisms of State-of-Charge (SOC) estimation is critical for both academic pursuits and practical applications in electric vehicles, renewable energy systems, and consumer electronics. The “Battery State-of-Charge (SOC) Estimation” course offered on Coursera, which also fulfills academic credit as ECEA 5732 for CU Boulder’s Master of Science in Electrical Engineering program, equips learners with essential knowledge and practical skills in this specialized area. **Course Structure and Content** The course is thoughtfully structured into several weekly modules, each designed to build on the knowledge gained in previous weeks. Below, I will provide an overview of each week’s focus: 1. **The Importance of a Good SOC Estimator**: The course kicks off by establishing a solid foundation in the concepts of SOC estimation. It covers fundamental definitions and introduces basic methods, highlighting their limitations. This week also delves into probability theory to equip students with tools for managing uncertainties in battery management systems (BMS). 2. **Introducing the Linear Kalman Filter as a State Estimator**: Here, learners are introduced to the theoretical framework of the Kalman filter, a pivotal methodology in state estimation. This week emphasizes understanding the derivation of the linear Kalman filter and the implications of its assumptions, preparing students for practical applications. 3. **Coming to Understand the Linear Kalman Filter**: This module focuses on providing intuitive insights into how the linear Kalman filter operates. Learners practice implementing the filter in Octave, a key programming environment, enhancing their coding skills alongside theoretical knowledge. 4. **Cell SOC Estimation Using an Extended Kalman Filter**: As battery cells are often nonlinear systems, this week transitions into implementing the extended Kalman filter (EKF). Students learn to adjust the linear methods to better fit battery systems, employing Octave to practically estimate SOC. 5. **Cell SOC Estimation Using a Sigma-Point Kalman Filter**: Building on the previous week, this module tackles the limitations of the EKF and introduces the sigma-point Kalman filter (or unscented Kalman filter). Students derive its principles and practice coding it for SOC estimation, expanding their toolkit for handling nonlinear systems. 6. **Improving Computational Efficiency Using the Bar-Delta Method**: As students navigate through more complex estimators, this module addresses real-world complications, such as constant bias errors in sensors. It introduces the bar-delta method for enhancing computational efficiency when working with extensive battery cell configurations. 7. **Capstone Project**: This final module is a hands-on experience, requiring students to apply their cumulative knowledge by tuning both the EKF and SPKF for SOC estimation. This project solidifies the learners’ understanding and provides practical skills essential for the industry. **Why You Should Take This Course** - **Comprehensive Curriculum**: The structured layout of the syllabus ensures that learners can progressively build their knowledge, making it accessible for both novice and experienced individuals in the field. - **Practical Skills**: The hands-on approach with Octave coding encourages students to actively engage with the material, leading to a deeper understanding of theoretical concepts through practical implementation. - **Industry Relevance**: As battery technology continues to advance, knowledge in SOC estimation is increasingly vital. This course targets not only academic excellence but also provides skills applicable in various industries including automotive, aerospace, and renewable energy. - **Expert Instructors**: Guided by knowledgeable faculty from CU Boulder, students benefit from high-quality instruction and support throughout the course. **Conclusion and Recommendations** The “Battery State-of-Charge (SOC) Estimation” course on Coursera is a highly recommendable offering for professionals and students alike who are keen to advance their understanding of battery management systems and state estimation techniques. With its comprehensive syllabus, practical coding components, and academic credibility, this course is an investment in your knowledge and career in the growing field of battery technology. Whether you aim to enhance your technical skills for career advancement or simply wish to gain a deeper understanding of battery operations, this course is a valuable stepping stone. Enroll today and take your first step towards mastering SOC estimation!

Syllabus

The importance of a good SOC estimator

This week, you will learn some rigorous definitions needed when discussing SOC estimation and some simple but poor methods to estimate SOC. As background to learning some better methods, we will review concepts from probability theory that are needed to be able to deal with the impact of uncertain noises on a system's internal state and measurements made by a BMS.

This week, you will learn how to derive the steps of the Gaussian sequential probabilistic inference solution, which is the basis for all Kalman-filtering style state estimators. While this content is highly theoretical, it is important to have a solid foundational understanding of these topics in practice, since real applications often violate some of the assumptions that are made in the derivation, and we must understand the implication this has on the process. By the end of the week, you will know how to derive the linear Kalman filter.

The steps of a Kalman filter may appear abstract and mysterious. This week, you will learn different ways to think about and visualize the operation of the linear Kalman filter to give better intuition regarding how it operates. You will also learn how to implement a linear Kalman filter in Octave code, and how to evaluate outputs from the Kalman filter.

A linear Kalman filter can be used to estimate the internal state of a linear system. But, battery cells are nonlinear systems. This week, you will learn how to approximate the steps of the Gaussian sequential probabilistic inference solution for nonlinear systems, resulting in the "extended Kalman filter" (EKF). You will learn how to implement the EKF in Octave code, and how to use the EKF to estimate battery-cell SOC.

The EKF is the best known and most widely used nonlinear Kalman filter. But, it has some fundamental limitations that limit its performance for "very nonlinear" systems. This week, you will learn how to derive the sigma-point Kalman filter (sometimes called an "unscented Kalman filter") from the Gaussian sequential probabilistic inference steps. You will also learn how to implement this filter in Octave code and how to use it to estimate battery cell SOC.

Kalman filtering requires that noises have zero mean. What do we do if the current-sensor has a dc bias error, as is often the case? How can we implement Kalman-filter type SOC estimators in a computationally efficient way for a battery pack comprising many cells? This week you will learn how to compensate for current-sensor bias error and how to implement the bar-delta method for computational efficiency. You will also learn about desktop validation as an approach for initial testing and tuning of BMS algorithms.

You have already learned that Kalman filters must be "tuned" by adjusting their process-noise, sensor-noise, and initial state-estimate covariance matrices in order to give acceptable performance over a wide range of operating scenarios. This final course module will give you some experience hand-tuning both an EKF and SPKF for SOC estimation.