A Battery Management System (BMS) is the central intelligence of every energy storage system. While battery cells store energy, the BMS determines how safely, efficiently, and reliably that energy can be used.

This course provides a practical, engineering-level introduction to how modern BMS architectures work, from battery modeling and state estimation to fault detection, cell balancing, and lifetime management. Participants will learn how electrical, thermal, and aging effects are incorporated into BMS algorithms and implemented in real battery packs.

Using MATLAB/Simulink-based modeling and simulation, participants will explore how BMS software monitors battery state, predicts available energy and power, and enforces safety limits under real-world operating conditions.

This course is designed for engineers, scientists, and technical professionals involved in:

• Electric vehicles and electrified powertrains
• Energy storage systems
• Battery pack design and integration
• Power electronics and control systems
• Battery testing, safety, and validation

It is ideal for those who already understand basic battery concepts and want to gain deeper insight into how batteries are actually managed and controlled in real systems.

• Explain the role of the BMS in maintaining battery safety, performance, and lifetime
• Understand how equivalent-circuit battery models are used to represent real cell behavior
• Estimate state of charge (SoC), state of health (SoH), and available power
• Understand how temperature, aging, and cell imbalance affect BMS decisions
• Evaluate cell balancing strategies and their impact on pack performance
• Identify and respond to fault conditions such as over-voltage, over-temperature, and sensor failure
• Analyze how BMS software interacts with chargers, inverters, and vehicle controllers

Includes 4 mini-projects to reinforce learning. MATLAB/Simulink tools provided.

Battery Modeling for BMS

• Open-circuit voltage and SoC relationships
• Equivalent-circuit models (Thevenin and RC networks)
• Temperature-dependent and aging-dependent behavior
• Parameter identification and model updating

State Estimation

• Coulomb counting and its limitations
• Model-based SoC estimation
• Internal resistance and power capability estimation
• Fundamentals of observer-based battery monitoring

State of Health and Aging

• Capacity fade and resistance growth
• How degradation impacts energy and power limits
• Incorporating aging into BMS decision-making

Power and Thermal Management

• Charge and discharge power limits
• Temperature-dependent operating envelopes
• Thermal-electrical coupling in battery packs

Cell Balancing and Pack Control

• Passive and active balancing methods
• Cell-to-cell mismatch and its impact on usable energy
• Pack-level vs cell-level control strategies

Fault Detection and Safety

• Over-voltage, under-voltage, and over-temperature protection
• Sensor faults and diagnostic strategies
• Isolation, shutdown, and safe-state operation

Modeling and Simulation

Participants will use MATLAB/Simulink to explore and visualize BMS behavior, including:

• Battery equivalent-circuit models
• SoC and SoH estimation algorithms
• Temperature-dependent limits
• Cell imbalance and balancing logic
• Fault injection and system response

These tools allow participants to see how a real BMS predicts internal battery state and makes control decisions before hardware is ever built.

Ahmad Khan

Awad Ali Syed

Awad Ali Syed is a senior engineering leader with over 15 years of experience in battery systems, systems integration, embedded controls, functional safety, and electric vehicle (EV) technologies. He currently serves as an Engineering Group Manager at General Motors.

Prior to this, Awad held engineering leadership roles at Freudenberg EPower Systems, Our Next Energy, and Fiat Chrysler Automobiles, where he managed system architecture, BMS development across xEV platforms, clean-sheet software development, and led global functional safety initiatives. His earlier roles at Robert Bosch Battery Systems and General Motors (via iGATE) focused on diagnostic algorithm development, model-based design, and HiL validation for high-voltage battery systems.

Awad holds a Master of Science in Electrical Engineering from The Ohio State University, with a specialization in power electronics and control systems. His technical interests include model-based systems engineering (MBSE), battery diagnostics, safety-critical software, and scalable integration frameworks for next-generation mobility platforms.