Spezialisierung Algorithms for Battery Management Systems

This course can also be taken for academic credit as ECEA 5730, part of CU Boulder’s Master of Science in Electrical Engineering degree.

This course will provide you with a firm foundation in lithium-ion cell terminology and function and in battery-management-system requirements as needed by the remainder of the specialization. After completing this course, you will be able to: - List the major functions provided by a battery-management system and state their purpose - Match battery terminology to a list of definitions - Identify the major components of a lithium-ion cell and their purpose - Understand how a battery-management system “measures” current, temperature, and isolation, and how it controls contactors - Identify electronic components that can provide protection and specify a minimum set of protections needed - Compute stored energy in a battery pack - List the manufacturing steps of different types of lithium-ion cells and possible failure modes

This course can also be taken for academic credit as ECEA 5731, part of CU Boulder’s Master of Science in Electrical Engineering degree.

In this course, you will learn the purpose of each component in an equivalent-circuit model of a lithium-ion battery cell, how to determine their parameter values from lab-test data, and how to use them to simulate cell behaviors under different load profiles. By the end of the course, you will be able to: - State the purpose for each component in an equivalent-circuit model - Compute approximate parameter values for a circuit model using data from a simple lab test - Determine coulombic efficiency of a cell from lab-test data - Use provided Octave/MATLAB script to compute open-circuit-voltage relationship for a cell from lab-test data - Use provided Octave/MATLAB script to compute optimized values for dynamic parameters in model - Simulate an electric vehicle to yield estimates of range and to specify drivetrain components - Simulate battery packs to understand and predict behaviors when there is cell-to-cell variation in parameter values

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

This course can also be taken for academic credit as ECEA 5733, part of CU Boulder’s Master of Science in Electrical Engineering degree.

In this course, you will learn how to implement different state-of-health estimation methods and to evaluate their relative merits. By the end of the course, you will be able to: - Identify the primary degradation mechanisms that occur in lithium-ion cells and understand how they work - Execute provided Octave/MATLAB script to estimate total capacity using WLS, WTLS, and AWTLS methods and lab-test data, and to evaluate results - Compute confidence intervals on total-capacity estimates - Compute estimates of a cell’s equivalent-series resistance using lab-test data - Specify the tradeoffs between joint and dual estimation of state and parameters, and steps that must be taken to ensure robust estimates (honors)