Machine Learning in Battery Management Systems
( 25 Modules )

Module #1
Introduction to Battery Management Systems
Overview of BMS, importance, and challenges

Module #2
Machine Learning Basics
Introduction to machine learning, types, and applications

Module #3
Data Acquisition and Preprocessing for BMS
Overview of data types, data preprocessing, and feature engineering

Module #4
Supervised Learning for State of Charge (SOC) Estimation
ML algorithms for SOC estimation, including linear and nonlinear models

Module #5
Unsupervised Learning for Anomaly Detection
Clustering and density-based methods for anomaly detection in BMS

Module #6
State of Health (SOH) Estimation using Machine Learning
ML algorithms for SOH estimation, including regression and classification models

Module #7
Battery Aging Prediction using Machine Learning
ML algorithms for battery aging prediction, including regression and survival analysis models

Module #8
Deep Learning for Battery Management Systems
Introduction to deep learning, including CNNs, RNNs, and LSTMs for BMS applications

Module #9
Recurrent Neural Networks for Time Series Forecasting
RNNs and LSTMs for forecasting battery voltage, current, and temperature

Module #10
Convolutional Neural Networks for Image-Based Diagnostics
CNNs for image-based diagnostics and defect detection in batteries

Module #11
Long Short-Term Memory (LSTM) Networks for SOC and SOH Estimation
LSTM networks for SOC and SOH estimation, including sequence-to-sequence models

Module #12
Transfer Learning for Battery Management Systems
Transfer learning for BMS applications, including pre-trained models and fine-tuning

Module #13
Explainable AI for Battery Management Systems
Explainable AI techniques for BMS, including feature importance and interpretable models

Module #14
Battery Charging and Discharging Optimization using Machine Learning
ML algorithms for optimizing charging and discharging profiles for battery health and performance

Module #15
Cell Balancing and Fault Detection using Machine Learning
ML algorithms for cell balancing and fault detection, including clustering and classification models

Module #16
Case Study:Electric Vehicle Battery Management Systems
Real-world application of machine learning in electric vehicle BMS

Module #17
Case Study:Renewable Energy Systems with Battery Storage
Real-world application of machine learning in renewable energy systems with battery storage

Module #18
Machine Learning for Battery Fault Prediction
ML algorithms for predicting battery faults, including classification and regression models

Module #19
Battery Health Monitoring using Machine Learning
ML algorithms for monitoring battery health, including degradation and fault detection

Module #20
Machine Learning for Battery Performance Optimization
ML algorithms for optimizing battery performance, including efficiency and power delivery

Module #21
Real-Time Machine Learning for Battery Management Systems
Real-time ML algorithms and architectures for BMS applications

Module #22
Edge Computing and Machine Learning for Battery Management Systems
Edge computing and ML algorithms for BMS applications, including resource-constrained devices

Module #23
Cloud-Based Machine Learning for Battery Management Systems
Cloud-based ML algorithms and architectures for BMS applications, including scalability and reliability

Module #24
Security and Privacy in Machine Learning for Battery Management Systems
Security and privacy considerations for ML algorithms in BMS applications

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Machine Learning in Battery Management Systems career

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Machine Learning in Battery Management Systems ( 25 Modules )

Machine Learning in Battery Management Systems
( 25 Modules )