Course Summary
Learn how to accurately measure the State of Health of batteries and predict their remaining lifespan. This course covers the fundamentals of battery operation, the methods to measure battery State of Health, and the latest tools to predict battery life.Key Learning Points
- Understand the fundamental principles of battery operation
- Learn how to accurately measure the State of Health of batteries
- Get hands-on experience with battery testing tools and techniques
- Predict the remaining lifespan of batteries using advanced tools and models
Job Positions & Salaries of people who have taken this course might have
- USA: $85,000
- India: ₹8,00,000
- Spain: €40,000
- USA: $85,000
- India: ₹8,00,000
- Spain: €40,000
- USA: $70,000
- India: ₹6,00,000
- Spain: €30,000
- USA: $85,000
- India: ₹8,00,000
- Spain: €40,000
- USA: $70,000
- India: ₹6,00,000
- Spain: €30,000
- USA: $100,000
- India: ₹12,00,000
- Spain: €60,000
Related Topics for further study
Learning Outcomes
- Ability to measure the State of Health of batteries accurately
- Knowledge of advanced tools and models to predict remaining battery lifespan
- Hands-on experience with battery testing techniques
Prerequisites or good to have knowledge before taking this course
- Basic understanding of electricity and electrical circuits
- Access to a computer with internet connection
Course Difficulty Level
IntermediateCourse Format
- Online self-paced course
- Video lectures
- Hands-on exercises
- Quizzes and assignments
Similar Courses
- Battery Technology
- Introduction to Energy Storage
- Renewable Energy and Green Building Entrepreneurship
Related Education Paths
Notable People in This Field
- Elon Musk
- Bill Gates
- Amory Lovins
Related Books
Description
This course can also be taken for academic credit as ECEA 5733, part of CU Boulder’s Master of Science in Electrical Engineering degree.
Knowledge
- H​ow to implement state-of-health (SOH) estimators for lithium-ion battery cells
Outline
- How does lithium-ion cell health degrade?
- 4.1.1: Welcome to the course!
- 4.1.2: What changes as a cell ages?
- 4.1.3: Negative-electrode aging processes at particle surface
- 4.1.4: Negative-electrode aging processes in bulk and composite electrode
- 4.1.5: Positive-electrode aging processes
- 4.1.6: Sensitivity of cell voltage to changes in equivalent series resistance (ESR)
- 4.1.7: Sensitivity of cell voltage to changes in cell total capacity
- 4.1.8: Summary of "How does lithium-ion cell health degrade?"; what next?
- Notes for lesson 4.1.1
- Frequently asked questions
- Course resources
- How to use discussion forums
- Earn a course certificate
- Are you interested in earning an MSEE degree?
- Notes for lesson 4.1.2
- Notes for lesson 4.1.3
- Notes for lesson 4.1.4
- Notes for lesson 4.1.5
- Notes for lesson 4.1.6
- Notes for lesson 4.1.7
- Notes for lesson 4.1.8
- Practice quiz for lesson 4.1.2
- Practice quiz for lesson 4.1.3
- Practice quiz for lesson 4.1.4
- Practice quiz for lesson 4.1.5
- Practice quiz for lesson 4.1.6
- Practice quiz for lesson 4.1.7
- Quiz for week 1
- Total-least-squares battery-cell capacity estimation
- 4.2.1: What’s wrong with using ordinary least squares to estimate total capacity?
- 4.2.2: How to find the ordinary-least-squares solution as a benchmark
- 4.2.3: Making the ordinary-least-squares solution computationally efficient
- 4.2.4: Setting up weighted total-least-squares solution
- 4.2.5: Finding the solution to a weighted total-least-squares problem
- 4.2.6: Confidence intervals on least-squares solutions
- 4.2.7: Summary of "Total-least-squares battery-cell capacity estimation"; what next?
- Notes for lesson 4.2.1
- Notes for lesson 4.2.2
- Notes for lesson 4.2.3
- Notes for lesson 4.2.4
- Notes for lesson 4.2.5
- Notes for lesson 4.2.6
- Notes for lesson 4.2.7
- Practice quiz for lesson 4.2.1
- Practice quiz for lesson 4.2.2
- Practice quiz for lesson 4.2.3
- Practice quiz for lesson 4.2.4
- Practice quiz for lesson 4.2.5
- Practice quiz for lesson 4.2.6
- Quiz for week 2
- Simplified total-least-squares battery-cell capacity estimates
- 4.3.1: Simplifying the total-least-squares solution for cases having proportional uncertainties
- 4.3.2: Making simplified solution computationally efficient
- 4.3.3: Defining geometry for approximate full solution to weighted total least squares
- 4.3.4: Finding appropriate cost function for approximate full solution to WTLS problem
- 4.3.5: Finding solution to the AWTLS problem
- 4.3.6: Adding fading memory
- 4.3.7: Summary of "Simplified total-least-squares battery-cell capacity estimates"; what next?
- Notes for lesson 4.3.1
- Notes for lesson 4.3.2
- Notes for lesson 4.3.3
- Notes for lesson 4.3.4
- Notes for lesson 4.3.5
- Notes for lesson 4.3.6
- Notes for lesson 4.3.7
- Practice quiz for lesson 4.3.1
- Practice quiz for lesson 4.3.2
- Practice quiz for lesson 4.3.3
- Practice quiz for lesson 4.3.4
- Practice quiz for lesson 4.3.5
- Practice quiz for lesson 4.3.6
- Quiz for week 3
- How to write code for the different total-capacity estimators
- 4.4.1: Introducing Octave code to estimate cell total capacity
- 4.4.2: Demonstrating Octave code for HEV: Scenario 1
- 4.4.3: Demonstrating Octave code for HEV: Scenarios 2–3
- 4.4.4: Demonstrating Octave code for BEV: Scenario 1
- 4.4.5: Demonstrating Octave code for BEV: Scenarios 2–3
- 4.4.6: Summary of "How to write code for the different total-capacity estimators"; what next?
- Notes for lesson 4.4.1
- Notes for lesson 4.4.2
- Notes for lesson 4.4.3
- Notes for lesson 4.4.4
- Notes for lesson 4.4.5
- Notes for lesson 4.4.6
- Practice quiz for lesson 4.4.1
- Practice quiz for lesson 4.4.2
- Practice quiz for lesson 4.4.3
- Practice quiz for lesson 4.4.4
- Practice quiz for lesson 4.4.5
- Quiz for week 4
- A Kalman-filter approach to total capacity estimation
- 4.5.1: Deriving SPKF method for parameter estimation
- 4.5.2: Deriving EKF method for parameter estimation
- 4.5.3: How to estimate states and parameters at the same time
- 4.5.4: Defining the steps for EKF and SPFK joint and dual estimation
- 4.5.5: Addressing issues of robustness and speed
- 4.5.6: Summary of "A Kalman-filter approach to total capacity estimation"; what next?
- Notes for lesson 4.5.1
- Notes for lesson 4.5.2
- Notes for lesson 4.5.3
- Notes for lesson 4.5.4
- Notes for lesson 4.5.5
- Notes for lesson 4.5.6
- Quiz for lesson 4.5.1
- Quiz for lesson 4.5.2
- Quiz for lessons 4.5.3 and 4.5.4
- Quiz for lesson 4.5.5
- Capstone project
Summary of User Reviews
Battery State of Health course on Coursera has received positive reviews from users. Many users found the course to be informative and engaging. The course covers various aspects of battery technology and its state of health, providing a comprehensive understanding of the subject.Key Aspect Users Liked About This Course
The course provides a comprehensive understanding of battery technology and its state of health.Pros from User Reviews
- The course is informative and engaging
- The instructors are knowledgeable and experienced
- The course covers various aspects of battery technology
- The course provides a comprehensive understanding of battery health
Cons from User Reviews
- Some users found the course to be too technical
- The course may not be suitable for beginners
- The course does not provide hands-on experience
- Some users found the course content to be repetitive
Gregory Plett
4.8 Raiting
Battery State-of-Health (SOH) Estimation
- 4.8
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This course can also be taken for academic credit as ECEA 5733, part of CU Boulder’s Master of Science in Electrical Engineering degree....
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This course can also be taken for academic credit as ECEA 5734, part of CU Boulder’s Master of Science in Electrical Engineering degree....
