Introduction

Battery Management Systems (BMS) are at the heart of modern energy storage solutions, powering innovations across electric vehicles (EVs), renewable energy systems, and smart grid technologies. With the rapid global shift toward clean energy, electrification, and sustainability, the demand for advanced BMS expertise has surged. Battery Management Systems (BMS) Training Course is designed to equip learners with cutting-edge knowledge in battery analytics, real-time monitoring, state-of-charge (SOC) estimation, thermal management, and predictive maintenance, ensuring optimal battery performance, safety, and longevity. Participants will gain hands-on exposure to AI-driven battery diagnostics, IoT-enabled battery monitoring, and high-performance energy storage systems, preparing them for industry-ready roles.

The course integrates industry-relevant case studies, practical simulations, and real-world applications to provide a comprehensive understanding of BMS architecture, algorithms, and design principles. Emphasizing lithium-ion battery technology, EV battery systems, renewable integration, and advanced safety protocols, this program bridges the gap between theoretical concepts and practical implementation. Learners will develop expertise in data-driven battery optimization, fault detection, and energy efficiency strategies, enabling them to contribute effectively to the rapidly evolving energy ecosystem.

Programme Curriculum

Battery Management Systems (BMS) Training Course

Introduction

Course Duration

5 days

Course Objectives

Understand advanced Battery Management System architecture and components
Analyze lithium-ion battery chemistry and performance optimization
Implement State of Charge (SOC) and State of Health (SOH) estimation algorithms
Explore thermal management and battery safety systems
Develop real-time battery monitoring using IoT and embedded systems
Apply AI and machine learning for predictive battery maintenance
Design cell balancing techniques for high-efficiency energy storage
Evaluate battery degradation and lifecycle management strategies
Integrate BMS with electric vehicles (EVs) and renewable energy systems
Understand battery fault diagnostics and failure analysis
Work with CAN communication protocols and embedded controllers
Optimize energy efficiency and smart grid integration
Gain expertise in industry-standard BMS software tools and simulations

Target Audience

Electrical and Electronics Engineering Students
EV and Automotive Engineers
Renewable Energy Professionals
Embedded Systems Developers
IoT and Data Analytics Engineers
Research Scholars in Energy Storage
Battery Manufacturing Professionals
Industry Professionals transitioning to clean energy technologies

Course Modules

Module 1: Fundamentals of Battery Technology

Types of batteries-Lithium-ion, Lead-acid, Solid-state
Electrochemical principles and reactions
Battery performance parameters
Charging and discharging cycles
Case Study: Evolution of EV battery technologies

Module 2: BMS Architecture and Design

BMS components and block diagram
Hardware and software integration
Centralized vs distributed BMS
Safety mechanisms
Case Study: Tesla BMS architecture

Module 3: Battery Modeling Techniques

Equivalent circuit models
Mathematical modeling
Simulation tools
Parameter estimation
Case Study: MATLAB battery modeling

Module 4: State Estimation Techniques

SOC estimation methods
SOH prediction models
Kalman filtering
Data-driven estimation
Case Study: AI-based SOC estimation

Module 5: Thermal Management Systems

Heat generation in batteries
Cooling techniques
Thermal runaway prevention
Design considerations
Case Study: EV battery cooling systems

Module 6: Cell Balancing Techniques

Passive vs active balancing
Efficiency optimization
Circuit design
Performance analysis
Case Study: Battery pack balancing in EVs

Module 7: Battery Safety and Protection

Over-voltage and under-voltage protection
Short circuit protection
Fault detection
Safety standards
Case Study: Battery failure incidents

Module 8: Communication Protocols

CAN bus fundamentals
Data acquisition systems
Communication interfaces
Real-time monitoring
Case Study: Automotive CAN networks

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

BMS Design Battery Monitoring State-of-Charge Estimation Cell Balancing

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

Oct 19 2026

19 Oct — 23 Oct 2026

💻 Online / Virtual session

Online $1,000