Introduction

Battery degradation analysis is a critical discipline in the rapidly evolving fields of electric vehicles (EVs), renewable energy storage, lithium-ion technology, and battery management systems (BMS). As global demand for sustainable energy solutions, fast-charging batteries, and high-performance energy storage systems increases, understanding the mechanisms behind battery aging, capacity fade, thermal instability, and performance degradation has become essential. Battery Degradation Analysis Training Course is designed to equip professionals with advanced analytical tools, predictive modeling techniques, and real-world diagnostic skills to assess and mitigate battery degradation effectively.

This course emphasizes data-driven battery analytics, AI-powered diagnostics, electrochemical modeling, and lifecycle optimization strategies. Participants will gain hands-on experience in failure analysis, state-of-health (SOH) estimation, degradation forecasting, and safety assessment, ensuring they can contribute to cutting-edge innovations in EV battery systems, grid storage solutions, and next-generation energy technologies.

Programme Curriculum

Battery Degradation Analysis Training Course

Introduction

Course Duration

5 days

Course Objectives

Understand battery degradation mechanisms in lithium-ion systems
Analyze capacity fade, internal resistance growth, and cycle aging
Apply data analytics and machine learning for battery diagnostics
Evaluate state-of-health (SOH) and state-of-charge (SOC) estimation
Perform thermal runaway and safety risk assessments
Develop predictive maintenance strategies using AI models
Interpret electrochemical impedance spectroscopy (EIS) data
Conduct battery lifecycle and performance optimization studies
Identify failure modes in EV and energy storage batteries
Utilize digital twin technology for battery simulation
Implement fast-charging degradation mitigation techniques
Assess sustainability and recycling impact on battery lifespan
Integrate battery analytics into smart grid and IoT systems

Target Audience

Battery engineers and EV design specialists
Energy storage system professionals
Data scientists in battery analytics
R&D engineers in lithium-ion technology
Automotive and EV industry professionals
Renewable energy and grid storage experts
Quality assurance and reliability engineers
Academics and researchers in electrochemistry

Course Modules

Module 1: Fundamentals of Battery Technology

Battery types and chemistries
Lithium-ion fundamentals
Energy density vs power density
Battery components overview
Case Study: EV battery architecture analysis

Module 2: Degradation Mechanisms

Chemical and mechanical degradation
SEI layer formation
Lithium plating effects
Electrolyte decomposition
Case Study: Capacity fade in EV batteries

Module 3: Battery Aging Analysis

Calendar vs cycle aging
Aging indicators
Performance degradation curves
Environmental impact
Case Study: Aging trends in grid storage systems

Module 4: Data-Driven Diagnostics

Data acquisition systems
Sensor integration
Data preprocessing techniques
Feature extraction
Case Study: Real-time battery monitoring system

Module 5: Machine Learning Applications

Predictive modeling
Regression and classification models
Neural networks for SOH prediction
AI-based diagnostics
Case Study: AI-driven battery failure prediction

Module 6: State Estimation Techniques

SOC estimation methods
SOH estimation models
Kalman filtering
Adaptive algorithms
Case Study: SOC estimation in EVs

Module 7: Thermal Management & Safety

Heat generation mechanisms
Thermal runaway analysis
Cooling strategies
Safety standards
Case Study: Battery fire incident analysis

Module 8: Electrochemical Analysis

EIS fundamentals
Voltage and current analysis
Reaction kinetics
Modeling techniques
Case Study: EIS-based degradation 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

Degradation Modeling Battery Health Assessment Cycle Life Analysis Performance Optimization

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