Developing Battery Management Systems with Simulink and Model-Based Design

As industries increasingly rely on battery energy storage, the role of the Battery Management System (BMS) has never been more critical. A well-designed BMS ensures safe operation, maximizes performance, and extends the lifespan of battery packs under real-world conditions.

This whitepaper describes how engineers develop BMS algorithms and software by performing system-level simulations with Simulink®. Model-Based Design enables you to gain insight into the dynamic behavior of the battery pack, explore software architectures, test operational cases, and begin hardware testing early—reducing costly design errors.

With this approach, the BMS model becomes the foundation for all design and development activities, including:

• Desktop simulation of functional aspects
• Formal verification and validation to industry standards
• Code generation for real-time simulation and hardware implementation

Why It Matters

Battery Management Systems are central to the safe, efficient, and long-lasting operation of modern energy storage. A model-based workflow empowers engineers to:

• Detect and resolve design issues earlier
• Accelerate prototyping and testing
• Transition smoothly into production-ready implementations
• Meet industry certification standards with confidence

What You’ll Learn

This whitepaper takes you inside the end-to-end process of BMS development with Simulink®, showing how engineers design, test, and implement advanced control software:

• Battery Modeling – Build accurate equivalent circuit models of cells, modules, and packs to capture electro-thermal behavior and interactions with surrounding circuits.
• Supervisory Control Algorithms – Use Stateflow® to design logic for cell balancing, fault detection, charge/discharge control, and thermal management.
• State-of-Charge and State-of-Health Estimation – Apply advanced techniques such as extended Kalman filters to track battery status under real-world conditions.
• Desktop Simulation – Explore design ideas, verify charge–discharge behavior, test balancing strategies, and run safety-critical requirements before building hardware.
• Real-Time Simulation & Rapid Prototyping – Generate C and HDL code automatically to test algorithms on real-time computers and interact with hardware in hours, not days.
• Hardware-in-the-Loop (HIL) Testing – Validate controllers in a safe virtual environment that replicates battery packs, circuits, and loads—carrying test cases over from desktop sim.
• Hardware Implementation – Generate efficient, production-ready code for certified deployment in automotive, aerospace, and other regulated industries.

Get the Full Story

Fill out the form to download the whitepaper and discover how simulation-driven design accelerates BMS development – from concept to certified hardware.

Featured products

MathWorks® products:

• MATLAB
• Simulink
• Simscape Electrical
• Simulink Real-Time
• Stateflow

Speedgoat products:

• Speedgoat target hardware

Learn more

• Blog: Developing Battery Systems with Simulink, Simscape, and Model-Based Design
  Discover how Simulink & Simscape enable advanced battery system design, from cell modeling to pack integration, with tools for faster development, safety, and multi-domain optimization.
  Read the blog
• On-demand webinar: Simulink for Next-Gen Efficient Battery Management Systems Development
  This webinar is designed for decision-makers who are evaluating how to modernize their software development processes. Instead of diving into technical details, we’ll walk through what the typical BMS development lifecycle looks like — and highlight where and how Simulink adds value throughout that process.
  Watch the on-demand webinar
• On-demand webinar: AI for Model-Based Design: Reduced Order Modeling
  Learn to distill complex systems into simplified models; ROM streamlines the design process, making simulations faster, more efficient, and easier to manage.
  Watch the on-demand webinar
• On-demand webinar: Reinforcement Learning Workflow in MATLAB
  Learn how to apply Reinforcement Learning using MATLAB® and Simulink® products
  Watch the on-demand webinar
• On-demand webinar: AI for Model-based Design: Virtual Sensor Modeling
  Learn to develop virtual sensor models using advanced AI techniques such as feedforward neural networks, LSTMs, and decision trees.
  Watch the on-demand webinar