Battery State-of-Charge (SOC) Estimation from Coursera

This course can also be taken for academic credit as ECEA 5732, part of CU Boulder’s Master of Science in Electrical Engineering degree.

In this course, you will learn how to implement different state-of-charge estimation methods and to evaluate their relative merits. By the end of the course, you will be able to:

- Implement simple voltage-based and current-based state-of-charge estimators and understand their limitations

- Explain the purpose of each step in the sequential-probabilistic-inference solution

- Execute provided Octave/MATLAB script for a linear Kalman filter and evaluate results

- Execute provided Octave/MATLAB script for state-of-charge estimation using an extended Kalman filter on lab-test data and evaluate results

- Execute provided Octave/MATLAB script for state-of-charge estimation using a sigma-point Kalman filter on lab-test data and evaluate results

- Implement method to detect and discard faulty voltage-sensor measurements

What's inside

Syllabus

The importance of a good SOC estimator

This week, you will learn some rigorous definitions needed when discussing SOC estimation and some simple but poor methods to estimate SOC. As background to learning some better methods, we will review concepts from probability theory that are needed to be able to deal with the impact of uncertain noises on a system's internal state and measurements made by a BMS.

Introducing the linear Kalman filter as a state estimator

This week, you will learn how to derive the steps of the Gaussian sequential probabilistic inference solution, which is the basis for all Kalman-filtering style state estimators. While this content is highly theoretical, it is important to have a solid foundational understanding of these topics in practice, since real applications often violate some of the assumptions that are made in the derivation, and we must understand the implication this has on the process. By the end of the week, you will know how to derive the linear Kalman filter.

Coming to understand the linear Kalman filter

The steps of a Kalman filter may appear abstract and mysterious. This week, you will learn different ways to think about and visualize the operation of the linear Kalman filter to give better intuition regarding how it operates. You will also learn how to implement a linear Kalman filter in Octave code, and how to evaluate outputs from the Kalman filter.

Cell SOC estimation using an extended Kalman filter

A linear Kalman filter can be used to estimate the internal state of a linear system. But, battery cells are nonlinear systems. This week, you will learn how to approximate the steps of the Gaussian sequential probabilistic inference solution for nonlinear systems, resulting in the "extended Kalman filter" (EKF). You will learn how to implement the EKF in Octave code, and how to use the EKF to estimate battery-cell SOC.

Cell SOC estimation using a sigma-point Kalman filter

The EKF is the best known and most widely used nonlinear Kalman filter. But, it has some fundamental limitations that limit its performance for "very nonlinear" systems. This week, you will learn how to derive the sigma-point Kalman filter (sometimes called an "unscented Kalman filter") from the Gaussian sequential probabilistic inference steps. You will also learn how to implement this filter in Octave code and how to use it to estimate battery cell SOC.

Improving computational efficiency using the bar-delta method

Kalman filtering requires that noises have zero mean. What do we do if the current-sensor has a dc bias error, as is often the case? How can we implement Kalman-filter type SOC estimators in a computationally efficient way for a battery pack comprising many cells? This week you will learn how to compensate for current-sensor bias error and how to implement the bar-delta method for computational efficiency. You will also learn about desktop validation as an approach for initial testing and tuning of BMS algorithms.

Capstone project

You have already learned that Kalman filters must be "tuned" by adjusting their process-noise, sensor-noise, and initial state-estimate covariance matrices in order to give acceptable performance over a wide range of operating scenarios. This final course module will give you some experience hand-tuning both an EKF and SPKF for SOC estimation.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Instructs learners in implementing state-of-charge estimation methods and evaluating them for merit in an array of applications

Appropriate for advanced learners who want to deepen their knowledge of state-of-charge estimation and battery technology

Gregory Plett's expertise in battery technology enhances the course's credibility

Provides strong foundational training in state-of-charge estimation methods

May require supplemental study in probability theory and linear algebra to fully grasp the advanced concepts

Learners should expect to invest significant time and effort to complete this course successfully

Reviews summary

Battery state-of-charge estimation

According to students, Battery State-of-Charge (SOC) Estimation is a phenomenal course that provides advanced instruction in a challenging but rewarding manner. Those who enroll say they particularly enjoy the engaging assignments, excellent instruction, and well-structured content. While a few students express difficulty with the complex mathematics, most agree the course material is presented in a clear, gradual, and easy-to-understand manner. Most agree this course is well worth their time and effort and a great choice for anyone interested in battery energy storage or electric vehicle technology.

Demanding but rewarding

"Capstone projects could be more demanding."

"Challenging theoretical and practical assignments."

"I got everthing I hoped for from this course."

Advanced

"Overall it was good course with detail explanation about state estimation using kalman filter, EKM and SPKF."

"This course is comprehensive introduction into the matter."

"A great foundational course for battery algorithms."

Excellent

"Wonderful course and truly knowledgeable and engaging instructor."

"Sir Gregory plett is an excellent Professor Ever and thanks to Coursera for such valuable plateform."

"The professor was capable to explain in a simple way such complex mathematics behind Kalman filters theory."

Clear and engaging

"The concepts taught were absolutely crucial for the later parts of this specialization and they were explained properly."

"The highlight of this course is that the professor explains all the complicated mathematics in small advancements that you can easily understand rather than putting a lot in front and confusing a lot."

"Even with minimum pre-knowledge, after the course ends, one is fully equipped to deal with ECM-based state-of-charges."

Highly challenging

"Good and a very challenging course."

"It's nice for the math development, maybe I think is very complex to perform this estimation in some real applications."

"A small variation could have been introduced in the project where one actually learns how to program Kalman filters."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Battery State-of-Charge (SOC) Estimation with these activities:

Review Calculus

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Refreshing your understanding of Calculus can help you better understand the mathematical modeling of batteries and battery management systems.

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Study your notes or textbook from your previous calculus courses
Work through practice problems on derivatives, integrals, and differential equations.
Take practice quizzes or exams to test your understanding.

Read 'Kalman Filtering: Theory and Practice with MATLAB' by Mohinder S. Grewal and Angus P. Andrews

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This book provides a comprehensive overview of Kalman Filter theory and its practical implementation in MATLAB.

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Read each chapter carefully and make notes.
Work through the MATLAB examples provided in the book.
Apply the concepts to your own Kalman Filter projects.

Follow Kalman Filter Tutorials

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Following tutorials on Kalman Filters can help you develop a deeper understanding of the theory and its implementation.

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Find online tutorials or courses on Kalman Filters.
Follow the tutorials step-by-step and implement the algorithms in a programming language of your choice.
Experiment with different filter parameters and observe their impact on the filter's performance.

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Solve Kalman Filter Practice Problems

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Solving practice problems can help you improve your problem-solving skills and apply the Kalman Filter theory to real-world scenarios.

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Find practice problems online or in textbooks.
Solve the problems using the Kalman Filter equations.
Analyze your results and compare them to the expected outputs.

Discuss Kalman Filter Applications

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Discussing applications of Kalman Filters with peers can broaden your understanding of its use cases and limitations.

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Find a study group or online forum where you can connect with other students learning about Kalman Filters.
Share your knowledge and experiences with the group.
Discuss different applications of Kalman Filters and their advantages and disadvantages.

Build a Kalman Filter Simulator

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Creating a Kalman Filter simulator can help you gain hands-on experience with the filter's implementation and behavior.

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Design a simple system model that you want to estimate the state of.
Implement the Kalman Filter algorithm in a programming language.
Simulate the system and observe how the Kalman Filter estimates the state over time.

Develop a Kalman Filter for a Real-World Problem

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Applying the Kalman Filter to a real-world problem can help you understand its practical implementation and impact.

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Identify a real-world problem that can benefit from Kalman Filter estimation.
Design and implement a Kalman Filter solution for the problem.
Evaluate the performance of your Kalman Filter and present your results.

Build a Battery Management System with Kalman Filter

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Building a Battery Management System with Kalman Filter can help you integrate your knowledge of Kalman Filters and battery management.

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Design the hardware and software architecture of your Battery Management System.
Implement the Kalman Filter algorithm for state estimation.
Test and validate your Battery Management System in a real-world setting.

Career center

Learners who complete Battery State-of-Charge (SOC) Estimation will develop knowledge and skills that may be useful to these careers:

Electrochemical Engineer

Electrochemical engineers are responsible for designing, developing, and testing electrochemical systems, such as batteries and fuel cells. This course provides a strong foundation in the fundamentals of electrochemistry, which is essential for a successful career in this field. The course covers topics such as battery SOC estimation, which is a key aspect of electrochemical engineering.

See salaries and explore the career path for Electrochemical Engineer

Research Scientist

Research scientists are responsible for conducting research in a variety of fields, including battery technology. This course provides a strong foundation in the fundamentals of battery technology, which is essential for a successful career as a research scientist in this field. The course covers topics such as battery SOC estimation, which is a key aspect of battery research.

See salaries and explore the career path for Research Scientist

Power Systems Engineer

Power systems engineers are responsible for designing, developing, and testing power systems, such as batteries and fuel cells. This course provides a strong foundation in the fundamentals of power systems, which is essential for a successful career in this field. The course covers topics such as battery SOC estimation, which is a key aspect of power systems engineering.

See salaries and explore the career path for Power Systems Engineer

Renewable Energy Engineer

Renewable energy engineers are responsible for designing, developing, and testing renewable energy systems, such as batteries and fuel cells. This course provides a strong foundation in the fundamentals of renewable energy, which is essential for a successful career in this field. The course covers topics such as battery SOC estimation, which is a key aspect of renewable energy engineering.

See salaries and explore the career path for Renewable Energy Engineer

Energy Storage Engineer

Energy storage engineers are responsible for designing, developing, and testing energy storage systems, such as batteries and fuel cells. This course provides a strong foundation in the fundamentals of energy storage, which is essential for a successful career in this field. The course covers topics such as battery SOC estimation, which is a key aspect of energy storage engineering.

See salaries and explore the career path for Energy Storage Engineer

Fuel Cell Engineer

Fuel cell engineers are responsible for designing, developing, and testing fuel cells. This course provides a strong foundation in the fundamentals of fuel cells, which is essential for a successful career in this field. The course covers topics such as battery SOC estimation, which is a key aspect of fuel cell engineering.

See salaries and explore the career path for Fuel Cell Engineer

Automotive Battery Engineer

Automotive battery engineers are responsible for designing and developing batteries for use in electric vehicles. This course puede ser muy útil if you seek a career as an automotive battery engineer. This course covers the fundamentals of battery technology that is relevant to this role, including SOC estimation. Whether you are a recent graduate seeking to enter this field or an experienced engineer seeking to move into this industry, this course may be able to provide you with the tools and skills you need to succeed.

See salaries and explore the career path for Automotive Battery Engineer

Battery Analyst

Battery analysts are responsible for analyzing battery data to identify trends and patterns. This course provides a strong foundation in the fundamentals of battery analysis, which is essential for a successful career in this field. The course covers topics such as battery SOC estimation, which is a key aspect of battery analysis.

See salaries and explore the career path for Battery Analyst

Battery Sales Engineer

Battery sales engineers are responsible for selling batteries to customers. This course provides a strong foundation in the fundamentals of battery technology, which is essential for a successful career in this field. The course covers topics such as battery SOC estimation, which is a key aspect of battery sales engineering.

See salaries and explore the career path for Battery Sales Engineer

Battery Marketing Manager

Battery marketing managers are responsible for developing and implementing marketing campaigns for batteries. This course provides a strong foundation in the fundamentals of battery technology, which is essential for a successful career in this field. The course covers topics such as battery SOC estimation, which is a key aspect of battery marketing.

See salaries and explore the career path for Battery Marketing Manager

Battery Management Engineer

A career in battery management requires a deep understanding of battery technology and performance. This course goes through many of the related topics and is directly relevant to the work of a battery management engineer. The estimation of a battery's SOC is a major concern for such a professional, and this course goes through this topic with extreme detail. Taking this course will help build a foundation towards a successful career in battery management.

See salaries and explore the career path for Battery Management Engineer

Battery Consultant

Battery consultants are responsible for providing advice to clients on battery-related matters. This course provides a strong foundation in the fundamentals of battery technology, which is essential for a successful career in this field. The course covers topics such as battery SOC estimation, which is a key aspect of battery consulting.

See salaries and explore the career path for Battery Consultant

Battery Design and Development Engineer

This course puede ser muy útil if you are seeking to work in the field of battery design and development. The topics covered by this course are fundamental to the work of such an engineer, and will help inform your design decisions. This course may help you to develop the expertise that such employers are seeking, as the skills and knowledge taught in this course are highly relevant to developing successful products within this industry.

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Batteries Engineer

Careers in Batteries Engineering require a strong grounding in electrochemistry at the undergraduate level. This course can help build that grounding to help advance your career. Because this course covers the fundamentals of batteries, whether you are a recent graduate seeking to enter this field or an experienced engineer seeking to move into this industry, this course may be able to provide you with the tools and skills you need to succeed. The mathematical rigor of this course also makes it a great way to prepare for advanced degrees in the field.

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Technical Writer

Technical writers are responsible for writing technical documentation, such as manuals, reports, and presentations. This course provides a strong foundation in the fundamentals of technical writing, which is essential for a successful career in this field. The course covers topics such as battery SOC estimation, which is a key aspect of technical writing in the battery industry.

See salaries and explore the career path for Technical Writer