Introduction

The rapid evolution of Artificial Intelligence (AI), Machine Learning (ML), and Predictive Analytics is transforming the energy sector, particularly in Battery Health Monitoring (BHM). With the global surge in Electric Vehicles (EVs), Renewable Energy Storage Systems, and Smart Grids, ensuring battery reliability, safety, and longevity has become mission-critical. AI-driven battery analytics enables real-time diagnostics, predictive maintenance, anomaly detection, and lifecycle optimization, significantly reducing operational risks and costs while improving performance efficiency.

AI for Battery Health Monitoring Training Course provides a comprehensive, industry-focused approach to leveraging Deep Learning, IoT-enabled sensors, Digital Twins, and Data-Driven Modeling for advanced battery health monitoring. Participants will gain hands-on expertise in state-of-charge (SOC) estimation, state-of-health (SOH) prediction, fault detection, and predictive failure modeling, equipping them with cutting-edge skills aligned with Industry 4.0, Smart Energy Systems, and Sustainable Technology Innovations.

Programme Curriculum

AI for Battery Health Monitoring Training Course

Introduction

Course Duration

5 days

Course Objectives

Understand AI-driven battery diagnostics and predictive analytics
Apply machine learning algorithms for battery degradation prediction
Develop real-time battery monitoring systems using IoT and edge AI
Master state-of-charge (SOC) and state-of-health (SOH) estimation techniques
Implement deep learning models for anomaly detection in batteries
Analyze big data from battery management systems (BMS)
Design predictive maintenance strategies for energy storage systems
Utilize digital twin technology for battery lifecycle simulation
Explore cloud-based AI platforms for scalable battery monitoring
Integrate AI with electric vehicle (EV) battery systems
Enhance battery safety using intelligent fault detection models
Optimize battery performance using data-driven insights
Understand future trends in AI-powered energy storage solutions

Target Audience

Data Scientists and AI Engineers
Electrical and Electronics Engineers
Energy Storage Professionals
EV Industry Specialists
IoT and Embedded Systems Developers
Researchers and Academics in Energy Systems
Battery Management System (BMS) Engineers
Sustainability and Renewable Energy Professionals

Course Modules

Module 1: Fundamentals of Battery Technology

Types of batteries
Battery chemistry and working principles
Charging/discharging cycles
Degradation mechanisms
Case Study: Lithium-ion battery lifecycle in EVs

Module 2: Introduction to AI in Energy Systems

AI applications in energy
Machine learning basics
Data-driven modeling
AI tools and frameworks
Case Study: AI adoption in smart grids

Module 3: Battery Data Acquisition & IoT Integration

Sensor technologies
IoT architecture for BHM
Data logging techniques
Edge computing basics
Case Study: IoT-enabled battery monitoring system

Module 4: Data Preprocessing & Feature Engineering

Data cleaning techniques
Feature extraction
Time-series data handling
Normalization methods
Case Study: Preparing EV battery datasets

Module 5: State of Charge (SOC) Estimation

SOC fundamentals
AI-based estimation models
Kalman filters vs ML models
Accuracy improvement techniques
Case Study: SOC prediction using neural networks

Module 6: State of Health (SOH) Prediction

SOH indicators
Degradation modeling
ML regression models
Performance evaluation
Case Study: Predicting battery aging patterns

Module 7: Predictive Maintenance with AI

Maintenance strategies
Failure prediction models
Risk assessment
Cost optimization
Case Study: AI-based predictive maintenance in EV fleets

Module 8: Deep Learning for Battery Analytics

Neural networks overview
CNN & RNN applications
Time-series forecasting
Model training and tuning
Case Study: Deep learning for battery fault detection

Training Methodology

Interactive lectures and presentations.
Group discussions and brainstorming sessions.
Hands-on exercises using real-world datasets.
Role-playing and scenario-based simulations.
Analysis of case studies to bridge theory and practice.
Peer-to-peer learning and networking.
Expert-led Q&A sessions.
Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

Battery Health Prediction Machine Learning State-of-Health Estimation Predictive Diagnostics

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Sep 21 2026

21 Sep — 25 Sep 2026

💻 Online / Virtual session

Online $1,000