Enroll Course: https://www.coursera.org/learn/battery-state-of-charge
In today’s world, where electric vehicles and renewable energy storage systems are becoming increasingly prevalent, understanding battery management systems (BMS) is crucial. One of the core components of BMS is the State-of-Charge (SOC) estimation, which determines how much energy is left in a battery. If you’re looking to deepen your knowledge in this area, the Coursera course “Battery State-of-Charge (SOC) Estimation” is an excellent choice.
### Course Overview
This course, which can also be taken for academic credit as ECEA 5732 as part of CU Boulder’s Master of Science in Electrical Engineering degree, provides a comprehensive understanding of SOC estimation methods. It covers both theoretical foundations and practical implementations, making it suitable for both students and professionals.
### What You Will Learn
By the end of the course, you will be equipped to:
– Implement simple voltage-based and current-based SOC estimators and understand their limitations.
– Explain the purpose of each step in the sequential-probabilistic inference process.
– Derive and implement various Kalman filters, including the linear Kalman filter, extended Kalman filter (EKF), and sigma-point Kalman filter (SPKF).
– Improve computational efficiency in SOC estimation using the bar-delta method.
– Complete a capstone project that involves tuning filters for optimal performance.
### Syllabus Breakdown
The course is structured into several weeks, each focusing on different aspects of SOC estimation:
1. **The Importance of a Good SOC Estimator**: Understand the rigorous definitions and poor methods of SOC estimation.
2. **Introducing the Linear Kalman Filter**: Learn to derive the Gaussian sequential probabilistic inference solution.
3. **Understanding the Linear Kalman Filter**: Gain intuition on the operation of the filter and implement it in Octave.
4. **Cell SOC Estimation Using an Extended Kalman Filter**: Learn to estimate SOC for nonlinear systems using EKF.
5. **Cell SOC Estimation Using a Sigma-Point Kalman Filter**: Derive and implement SPKF for very nonlinear systems.
6. **Improving Computational Efficiency**: Address current-sensor bias errors and implement the bar-delta method.
7. **Capstone Project**: Gain hands-on experience in tuning SOC estimators.
### Why You Should Take This Course
This course stands out due to its blend of theoretical knowledge and practical application. The use of Octave for coding implementations allows students to gain hands-on experience, which is invaluable in the field of electrical engineering. Additionally, the capstone project provides an opportunity to apply what you’ve learned in a real-world context, enhancing your understanding and skills.
Whether you’re a student pursuing a degree in electrical engineering or a professional looking to enhance your expertise in battery management systems, this course is highly recommended. It not only equips you with essential skills but also prepares you for the challenges in the rapidly evolving field of energy storage.
In conclusion, the “Battery State-of-Charge (SOC) Estimation” course on Coursera is a must-take for anyone interested in battery technology and management. With its comprehensive syllabus and practical focus, it will undoubtedly enhance your knowledge and skills in this critical area.
Enroll Course: https://www.coursera.org/learn/battery-state-of-charge