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Battery State-of-Charge (SOC) Estimation


As the world moves towards electric vehicles and renewable energy, understanding battery management systems (BMS) becomes increasingly crucial. One key aspect of BMS is estimating the battery's State of Charge (SOC). The University of Colorado Boulder's Coursera course, Battery State-of-Charge (SOC) Estimation, is a valuable resource for anyone looking to deepen their knowledge in this area.

Course Overview

This course, led by Professor Gregory Plett, is the third installment in the "Algorithms for Battery Management Systems" specialization. It focuses on various methods of SOC estimation, covering both theoretical and practical aspects. The course is structured to provide a comprehensive understanding of SOC estimation techniques, emphasizing the importance of accurate estimations in managing battery health and efficiency.

Key Learning Outcomes

  1. Voltage-Based and Current-Based Estimations: Learn simple methods to estimate SOC and understand their limitations.
  2. Kalman Filters: Gain insights into linear Kalman filters, extended Kalman filters (EKF), and sigma-point Kalman filters (SPKF), which are pivotal for accurate SOC estimation in nonlinear systems.
  3. Practical Implementation: Execute MATLAB or Octave scripts provided in the course to implement SOC estimation methods and evaluate their effectiveness using real lab-test data.
  4. Fault Detection: Develop methods to detect and discard faulty voltage-sensor measurements, enhancing the reliability of SOC estimations.

Course Structure

The course is divided into several modules, each focusing on different aspects of SOC estimation:

  • Week 1: Introduction to SOC estimation, covering basic concepts and simple estimation methods.
  • Week 2: Introduction to linear Kalman filters, explaining the sequential probabilistic inference steps.
  • Week 3: Understanding the operation and implementation of linear Kalman filters.
  • Week 4: Extending Kalman filters to nonlinear systems for more accurate SOC estimation.
  • Week 5: Introduction to sigma-point Kalman filters, addressing the limitations of EKF in highly nonlinear systems.
  • Week 6: Improving computational efficiency using the bar-delta method, crucial for battery packs with multiple cells.
  • Week 7: Capstone project involving the tuning of EKF and SPKF for SOC estimation.
  • Week 8: Final exam to assess understanding and application of the course content.


To fully benefit from this course, it is recommended that learners have prior knowledge from the preceding courses in the specialization (ECEA 5730 and ECEA 5731), as well as a strong foundation in electrical, computer, or mechanical engineering principles. Proficiency in MATLAB or Octave is also essential for executing the course's practical components.

Why Enroll? Mastering Battery State-of-Charge Estimation with Coursera

Accurate SOC estimation is critical for the optimal performance and longevity of batteries in electric vehicles and other applications. This course not only equips you with the theoretical knowledge but also provides practical tools and techniques to implement and evaluate SOC estimation methods effectively. By the end of the course, you'll be able to contribute to advancements in battery management technology, making a significant impact in the field of renewable energy and electric mobility.

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