# Battery State-of-Health (SOH) Estimation

Battery State-of-Health (SOH) Estimation is a comprehensive course designed to equip learners with the skills to implement state-of-health (SOH) estimators for lithium-ion battery cells.

• Learn about primary degradation mechanisms in lithium-ion cells and their effects.
• Implement different state-of-health estimation methods using provided Octave/MATLAB scripts.
• Evaluate total capacity using WLS, WTLS, and AWTLS methods with lab-test data.
• Compute confidence intervals and equivalent-series resistance estimates.
• Understand the tradeoffs between joint and dual estimation of state and parameters.

This course provides a solid foundation for understanding the complexities of battery health estimation and is suitable for individuals interested in electrical engineering, battery technology, and energy storage systems.

Certificate Available ✔

##### Course Modules

Battery State-of-Health (SOH) Estimation comprises modules covering degradation mechanisms, capacity estimation methods, parameter estimation, and practical implementation using Octave/MATLAB scripts.

#### How does lithium-ion cell health degrade?

Module 1 introduces the primary degradation mechanisms in lithium-ion cells, covering negative and positive electrode aging processes, sensitivity of cell voltage to changes in equivalent series resistance (ESR) and total capacity, and the tradeoffs between joint and dual estimation of state and parameters.

#### Total-least-squares battery-cell capacity estimation

Module 2 delves into total-least-squares battery-cell capacity estimation, exploring the limitations of ordinary least squares, computational efficiency, weighted total-least-squares solution, confidence intervals, and benchmark solutions.

#### Simplified total-least-squares battery-cell capacity estimates

Module 3 focuses on simplified total-least-squares battery-cell capacity estimates, addressing cases with proportional uncertainties, computational efficiency, defining geometry for approximate full solution to weighted total least squares, and faded memory.

#### How to write code for the different total-capacity estimators

Module 4 teaches learners how to write code for different total-capacity estimators using Octave, with demonstrations for hybrid electric vehicle (HEV) and battery electric vehicle (BEV) scenarios.

#### A Kalman-filter approach to total capacity estimation

Module 5 covers a Kalman-filter approach to total capacity estimation, including the derivation of methods for parameter estimation, estimation of states and parameters simultaneously, and addressing issues of robustness and speed.

#### Capstone project

Module 6 involves a capstone project where learners can apply their knowledge in a practical setting, integrating the concepts and techniques learned throughout the course.

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