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Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
Coursera’s Battery State-of-Charge (SOC) Estimation course is the third part of the five-course Algorithms for Battery Management Systems Specialisation. It can also be taken as an academic credit course as ECEA 5732, which is part of the University of Columbia Boulder’s Master of Science in Electrical Engineering degree.
The Battery State-of-Charge (SOC) Estimation online programme trains you about the implementation of various state-of-charge estimation methods. It teaches you how to evaluate their corresponding merits for lithium-ion battery cells. You will be introduced to the sequential-probabilistic-inference solution and the Octave/MATLAB script for linear Kalman filter while also evaluating the results.
The intermediate-level Battery State-of-Charge (SOC) Estimation course by Coursera is offered by the University of Colombia Boulder and the University of Colombia Systems, which are well-known globally for their standard of education. The instructor for this course will be Professor Gregory Plett, who teaches Electrical and Computer engineering. At the end of the Battery State-of-Charge (SOC) Estimation course, you will work on the Capstone project, which will help you apply all the skills you have learned practically.
The Highlights
- Self-paced training
- Flexible deadlines
- Capstone project
- Course completion in approximately 27 hours
- Free enrolment
- Offered by University of Colorado Boulder
- Online course
- Shareable certificate
- Course delivery in English
- Intermediate-level course
- Financial assistance
Programme Offerings
- Shareable Electronic Certificate
- practice quizzes
- Free Enrolment
- Flexible Deadlines
- Online Course
- Self-paced training.
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | CU BoulderCoursera |
Candidates can join the Coursera Battery State-of-Charge (SOC) Estimation training program by clicking on the “Enroll for Free” tab free of cost. They will get access to the course material, but not the course completion certificate. However, to obtain a certificate, or to access the graded items, candidates have to purchase the certification experience. To purchase the course, candidates will have to pay the following fee.
Battery State-of-Charge (SOC) Estimation Course Fee
Particulars | Fee in INR |
| Battery State-of-Charge (SOC) Estimation (Audit Mode) | Free |
Battery State-of-Charge (SOC) Estimation - 1 month | Rs. 6,634 /- |
| Battery State-of-Charge (SOC) Estimation - 3 months | Rs. 13,268 /- |
| Battery State-of-Charge (SOC) Estimation - 6 months | Rs. 19,903 /- |
Eligibility Criteria
To be able to receive a certification for the Battery State-of-Charge (SOC) Estimation online programme, you need to fulfil the respective prerequisites and complete the module, which includes the Capstone project. After satisfying these requirements, you can obtain a Coursera course completion certificate. You can attach the certificate on your LinkedIn page or your CV/resume.
What you will learn
After completing all the modules and the quizzes of the Battery State-of-Charge (SOC) Estimation training, you will develop a skill set in the following domains:
- Discussion of SOC estimation using thorough definitions
- Understand the probability theory to deal with noises on a system’s measurements and internal state made by a BMS
- Use the Gaussian sequential probabilistic inference solution to derive steps for Kalman-filtering style state estimators
- Know various ways to perceive the operation of the linear Kalman filter and implement it in Octave code as well as evaluate outputs from it
- Learn how to derive the extended Kalman Filter (EKF) and implement it in Octave code and to estimate battery cell SOC
- Know how to implement a bar-delta technique for computational efficiency and compensate for current sensor bias error
- Know about desktop validation for tuning and initial testing of BMS algorithms
Who it is for
Admission Details
Candidates planning to enrol for Battery State-of-Charge (SOC) Estimation programme need to follow these steps:
- Visit the Course page.
- Type the course name “Battery State-of-Charge (SOC) Estimation” in the search bar.
- Choose the “Enroll for Free” option.
- Create a new account on Coursera or login using your Google, Facebook, or Apple ID. If you already have an account with Coursera, log in using your credentials and enrol for the course.
Application Details
It is not necessary to fill a separate application form to register the course. Join for free by signing up with Google/Facebook/Coursera credentials to get access to the learning material. Coursera provides a seven-day free trial option also.
The Syllabus
Videos
- Welcome to the course!
- What is the importance of a good SOC estimator?
- How do we define SOC carefully?
- What are some approaches to estimating battery cell SOC?
- Understanding uncertainty via mean and covariance
- Understanding joint uncertainty of two unknown quantities
- Understanding time-varying uncertain quantities
- Summary of "The importance of a good SOC estimator" and next steps
Readings
- Get help and meet other learners in this course. Join your discussion forums!
- Notes for lesson
- 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 3.1.2
- Notes for lesson 3.1.3
- Notes for lesson 3.1.4
- Introducing a new element to the course!
- Notes for lesson 3.1.5
- Notes for lesson 3.1.6
- Notes for lesson 3.1.7
- Notes for lesson 3.1.8
Quizzes
- Quiz for week 1
- Practice quiz for lesson 3.1.2
- Practice quiz for lesson 3.1.3
- Practice quiz for lesson 3.1.4
- Practice quiz for lesson 3.1.5
- Practice quiz for lesson 3.1.6
- Practice quiz for lesson 3.1.7
Videos
- Predict/correct mechanism of sequential probabilistic inference
- The Kalman-filter gain factor
- Summarizing the six steps of generic probabilistic inference
- Deriving the three Kalman-filter prediction steps
- Deriving the three Kalman-filter correction steps
- Summary of "Introducing the linear KF as a state estimator" and next steps
Readings
- Notes for lesson 3.2.1
- Notes for lesson 3.2.2
- Notes for lesson 3.2.3
- Notes for lesson 3.2.4
- Notes for lesson 3.2.5
- Notes for lesson 3.2.6
Quizzes
- Quiz for week 2
- Practice quiz for lesson 3.2.1
- Practice quiz for lesson 3.2.2
- Practice quiz for lesson 3.2.3
- Practice quiz for lesson 3.2.4
- Practice quiz for lesson 3.2.5
Videos
- Visualizing the Kalman filter with a linearized cell model
- Introducing Octave code to generate correlated random numbers
- Introducing Octave code to implement KF for linearized cell model
- How do we improve numeric robustness of Kalman filter?
- Can we automatically detect bad measurements with a Kalman filter?
- How do I initialize and tune a Kalman filter?
- Summary of "Coming to understand the linear KF" and next steps
Readings
- Notes for lesson 3.3.1
- Notes for lesson 3.3.2
- Notes for lesson 3.3.3
- Notes for lesson 3.3.4
- Notes for lesson 3.3.5
- Notes for lesson 3.3.6
- Notes for lesson 3.3.7
Quizzes
- Quiz for week 3
- Practice quiz for lesson 3.3.1
- Practice quiz for lesson 3.3.2
- Practice quiz for lesson 3.3.3
- Practice quiz for lesson 3.3.4
- Practice quiz for lesson 3.3.5
- Practice quiz for lesson 3.3.6
Videos
- Introducing nonlinear variations to Kalman filters
- Deriving the three extended-Kalman-filter prediction steps
- Deriving the three extended-Kalman-filter correction steps
- Introducing a simple EKF example, with Octave code
- Preparing to implement EKF on an ECM
- Introducing Octave code to initialize and control EKF for SOC estimation
- Introducing Octave code to update EKF for SOC estimation
- Summary of "Cell SOC estimation using an EKF" and next steps
Readings
- Notes for lesson 3.4.1
- Notes for lesson 3.4.2
- Notes for lesson 3.4.3
- Notes for lesson 3.4.4
- Notes for lesson 3.4.5
- Notes for lesson 3.4.6
- Notes for lesson 3.4.7
- Notes for lesson 3.4.8
Quizzes
- Quiz for week 4
- Practice quiz for lesson 3.4.1
- Practice quiz for lesson 3.4.2
- Practice quiz for lesson 3.4.3
- Practice quiz for lesson 3.4.4
- Practice quiz for lesson 3.4.5
- Practice quiz for lesson 3.4.7
Videos
- Problems with EKF that are improved with sigma-point methods
- Approximating uncertain variables using sigma points
- Deriving the six sigma-point-Kalman-filter steps
- Introducing a simple SPKF example with Octave code
- Introducing Octave code to initialize and control SPKF for SOC estimation
- Introducing Octave code to update SPKF for SOC estimation
- Summary of "Cell SOC estimation using a SPFK" and next steps
Quizzes
- Quiz for week 5
- Practice quiz for lesson 3.5.1
- Practice quiz for lesson 3.5.2
- Practice quiz for lesson 3.5.3
- Practice quiz for lesson 3.5.4
- Practice quiz for lesson 3.5.6
Readings
- Notes for lesson 3.5.1
- Notes for lesson 3.5.2
- Notes for lesson 3.5.3
- Notes for lesson 3.5.4
- Notes for lesson 3.5.5
- Notes for lesson 3.5.6
- Notes for lesson 3.5.7
Videos
- Why do we need to be clever when estimating SOC for battery packs?
- Developing a "bar" filter using an ECM
- Developing the "delta" filters using an ECM
- Introducing "desktop validation" as a method for predicting performance
- Summary of "Improving computational efficiency using the bar-delta method" and next steps
Readings
- Notes for lesson 3.6.1
- Notes for lesson 3.6.2
- Notes for lesson 3.6.3
- Notes for lesson 3.6.4
- Notes for lesson 3.6.5
Quizzes
- Quiz for lesson 3.6.1
- Quiz for lesson 3.6.2
- Quiz for lesson 3.6.3
- Quiz for lessons 3.6.4 and 3.6.5
Instructors
CU Boulder Frequently Asked Questions (FAQ's)
The specialisation will help you in acquiring major concepts such as the working of lithium-ion battery cells, mathematical modelling of behaviours, and writing up algorithms to estimate SOC, SOH remaining energy. Thus, you will be backed-up with all the essential information required in the field.
Yes, but it can only be used as part of the academic credit for CU Boulder’s Master of Science in electrical engineering degree course ECEA 5732.
The online course starts on the 9th of November 2020 and will last for seven weeks. It should take approximately 27 hours to complete it.
Yes. It will be conducted entirely online, and the courses of the Algorithms for Battery Management Systems count as credit courses.
Yes, Coursera does provide financial aid to candidates who cannot afford the fee. Fill in an application and you will be notified when the application is approved.