📚 Syllabus & Course Curriculum
AI & Machine Learning in Healthcare
Module-by-module breakdown of Machine Learning for Battery Lifetime and Degradation Analysis, from foundations to a certified capstone project.
Electrochemistry
• SEI growth, lithium plating, particle cracking and loss of active material
• Calendar versus cycle ageing and the stress factors driving each
• Distinguishing capacity fade from power fade in measured data
Data
• Public cycling datasets and their protocol differences
• Incremental capacity and differential voltage analysis as features
• Impedance spectroscopy features and practical measurement constraints
Prediction
• Early-cycle prediction of end-of-life from the first hundred cycles
• Regression and sequence models for capacity trajectory
• Uncertainty quantification, because a point RUL estimate is not actionable
Hybrid
• Equivalent circuit and single-particle models as priors
• Physics-informed neural networks and hybrid model structures
• Extrapolating beyond the training envelope without fooling yourself
Deployment
• On-board estimation under BMS compute and memory constraints
• Fleet-level analytics and warranty exposure modelling
• Second-life screening and the safety implications of misclassification
Earn government-registered certification in Machine Learning for Battery Lifetime and Degradation Analysis
e-Certificate and e-Marksheet issued on successful completion.
