Enroll Course: https://www.coursera.org/learn/battery-state-of-health
As the world increasingly relies on batteries, understanding their health and longevity is paramount. The Coursera course, ‘Battery State-of-Health (SOH) Estimation,’ offered by CU Boulder, provides an exceptional deep dive into this critical area. This course is not just for electrical engineering students; it’s for anyone interested in the inner workings of batteries, from EV enthusiasts to renewable energy professionals.
The course begins by demystifying the degradation mechanisms of lithium-ion cells. You’ll gain a solid understanding of the physical and chemical processes that lead to capacity fade and resistance increase, explaining why accurately tracking capacity is more challenging than tracking resistance. This foundational knowledge is crucial for appreciating the subsequent estimation techniques.
What truly sets this course apart is its practical approach to SOH estimation. It moves beyond simplistic methods, introducing the Total-Least-Squares (TLS) approach as a more accurate alternative to Ordinary-Least-Squares (OLS). You’ll learn to derive weighted OLS and TLS solutions, providing a strong theoretical grounding.
The syllabus then delves into making these complex calculations efficient for real-world applications, particularly for Battery Management Systems (BMS). The progression from a proportionally weighted TLS to an ‘approximate weighted TLS’ (AWTLS) method is a masterclass in algorithm optimization for embedded systems. This practical aspect is invaluable for anyone looking to implement these techniques.
Furthermore, the course equips you with the coding skills to bring these methods to life. Using Octave/MATLAB, you’ll implement different estimators and benchmark their performance across various simulation scenarios relevant to hybrid and battery-electric vehicles. This hands-on coding experience solidifies your understanding and prepares you for practical challenges.
For those seeking an even deeper understanding, the course touches upon advanced Kalman filter techniques (EKF and SPKF) for simultaneously estimating battery state and parameters. The capstone project offers a final opportunity to apply all learned methods, emphasizing the importance of input data quality.
In conclusion, ‘Battery State-of-Health (SOH) Estimation’ is a comprehensive, practical, and intellectually stimulating course. It offers a rigorous yet accessible exploration of battery degradation and estimation, making it a highly recommended resource for anyone serious about battery technology.
Enroll Course: https://www.coursera.org/learn/battery-state-of-health