Hello, my name is Gregory Plett and it's my pleasure to welcome you to this course on Battery State of Health Estimation. This is the fourth course in a Coursera specialization on algorithms for battery management systems. By now you understand that battery management systems need to estimate non-measurable quantities that describe the condition of a battery pack. These quantities include things like the state of charge, but they also include different measures that describe in some way the health of battery cells and the health of the battery pack altogether. I reproduce in the illustration on this slide a figure that you've seen before to give you a sense of the progress that you have made so far in this specialization. Remember in the first course, you learned about the requirements of a battery management system, which include requirements for being able to measure all of this cell voltages, and the battery pack current, and also the module temperatures. In the second course, which is not really illustrated in this diagram, you learn how to make mathematical models or equations that describe battery cell behavior, and you learned how to predict how a cell would respond to profiles of input current versus time in order to produce a profile of output voltage versus time. In the third course, you learned how to combine knowledge from the sensor measurements and the models from the second course in order to make algorithms that would estimate battery cell state of charge. In this course, you'll additionally learn how to compute estimates of factors that describe the health of a battery pack. Finally, in the remaining course, you'll learn how to use your accumulated knowledge and skills to develop methods to balance battery cells in a battery pack, and how to compute power limits. In this course, you will learn the primary ways that lithium-ion battery cells degrade over time, and this motivates the need to be able to adjust the parameter values in battery cell models to track degradation as it progresses. In particular, you will learn how to estimate battery cell resistance as it changes over time. You will also learn how to estimate the battery cell total capacity as it changes over time. This second task turns out to be quite difficult to do and because of that, you will learn several different methods and some of the tradeoffs between them. If you choose to pursue the honors track in this course, you will also learn how to co-estimate the state of a cell and the health of the cell, in other words, the parameter values of a cell model at the same time, and you will use some Kalman filtering techniques that you learned in the third course applied to this problem in order to do so. After completing this course, you will be able to identify the primary degradation mechanisms that occur in lithium-ion battery cells, and understand how they work, and how they lead to a failure of this cell over time. You will learn how to execute some Octave code that I provide to you, to estimate total capacity using weighted least squares methods, and weighted total least squares methods, and variations on those. You will use data that's representative of data that you would measure in a laboratory or in a battery pack to exercise these algorithms to get an intuitive feel for when they work and when they fail and what kind of results you might expect from them. You will also learn how to compute confidence intervals on the capacity estimates that the algorithms produce, so that you will be able to use the estimate of total capacity within its tolerances to be able to predict things like power and energy with accuracy. You will also learn how to compute estimates of a battery cells equivalent series resistance using lab-test data as that resistance changes over time, and if you pursue the honors track, you will learn how to specify tradeoffs between different methods for co-estimating the state and the parameters of a battery cell, and these include methods known as joint estimation, and dual estimation, and you will use different types of Kalaman filters with those such as the extended Kalman filter and the sigma point Kalman filter. This course is intended to be taken forth in series in this specialization on algorithms for battery management systems. The topics in this course rely on background knowledge that you have gained from the earlier courses, and some of that being very specific pieces of knowledge, and some of it just more of a general understanding that you have developed from them. So, in particular it relies on some basic background information in battery management system requirements and sensing capabilities of battery management systems that you learned from the first course in the specialization. It relies on background topics regarding the enhanced self-correcting cell model, and what you learned about that regarding the Octave codes for creating that model, and for using that model that I provided to you in the second course in the specialization. It relies on the basics of probability theory and Kalman filtering that you learned about in the third course in the specialization, and especially if you choose to pursue the honors track, you will need to have significant background in nonlinear Kalman filtering to be able to follow and understand the material of that week of the course, because it goes very quickly. The contents of this course are patterned and after the fourth chapter of Battery Management Systems volume two on equivalent circuit methods. This book is available from our tech house. The book is not required but I do believe that it can provide you with a good permanent reference for later review and for further study as well. So, that brings us to the end of this introductory topic and again, just a really warm welcome from me. Thank you for having the interest in pursuing this course. I believe that you're going to learn some very valuable concepts for developing battery management system algorithms to estimate the state of health of battery cells. I really look forward to sharing these topics with you, and so let's get started right away going to the first topic.