Battery systems are becoming central to electric vehicles, eVTOL aircraft, ships, and battery energy storage systems, creating new demands for efficient modeling, control, thermal management, and validation.
This in-depth white paper from MathWorks explores how engineers can use Simulink and Simscape Battery to develop battery systems from cell-level models through complete battery packs, battery management systems, real-time simulation, and production-ready hardware implementation.
Rather than treating battery development as a series of disconnected engineering tasks, the white paper presents a model-based workflow that connects battery modeling, pack design, BMS algorithm development, desktop simulation, rapid prototyping, hardware-in-the-loop testing, and production code generation.
You will learn:
• How to model battery behavior using equivalent circuit, electrochemical, and data-driven models
• How to design battery packs from individual cells through modules and complete packs
• How to model thermal behavior and develop battery thermal management systems
• How to develop BMS algorithms for monitoring, estimation, balancing, charging, and protection
• How to estimate state of charge and state of health using model-based and data-driven approaches
• How desktop simulation can validate battery designs before hardware testing
• How rapid prototyping and hardware-in-the-loop testing accelerate BMS validation
• How Simulink can generate production-ready C/C++ and HDL code
• How processor-in-the-loop simulation can increase confidence before final deployment
The white paper begins with the battery system development workflow, showing how engineers can move from battery modeling to battery pack design, BMS algorithm development, closed-loop desktop simulation, real-time simulation, and finally hardware implementation and testing. This approach allows teams to evaluate battery behavior and BMS strategies earlier while reducing the need for repeated physical prototypes.
A major focus is battery modeling. MathWorks describes three primary modeling approaches: equivalent circuit models, electrochemical models, and data-driven models. Equivalent circuit models provide computationally efficient representations for system-level simulation and BMS development, while electrochemical models provide deeper insight into internal battery processes. Data-driven models use experimental or simulation data to capture behavior that may be difficult to represent analytically.
The white paper then examines battery pack design using the Battery Builder app and Simscape Battery API. Engineers can build and visualize battery models across different geometries and topologies, model cooling plates and thermal connections, evaluate cell-to-cell temperature variation, and select an appropriate model resolution to balance simulation fidelity with computational speed.
Thermal management is another critical element. Simscape Battery enables engineers to model thermal behavior from cell to pack, including temperature nonuniformity, thermal propagation, conduction, convection, and radiation. Reduced-order thermal models can also be derived from high-fidelity analyses to support system-level and real-time simulation. Cooling plate configurations and closed-loop controls can be incorporated to evaluate thermal management strategies under different operating conditions.
The white paper also explores battery management system algorithms, covering monitoring, state estimation, cell balancing, power management, thermal management, protection, and communications. These functions directly influence battery safety, efficiency, longevity, and overall system reliability.
For state estimation, the guide compares approaches including Coulomb counting, voltage-based methods, Kalman filters, and neural networks. Simscape Battery provides built-in SOC estimator blocks, while neural networks can be trained using current, voltage, temperature, and SOC data for data-driven estimation.
State of health estimation is also addressed, recognizing that batteries gradually lose capacity and develop higher internal resistance through calendar aging and cycling. Because organizations may define battery health differently, Simulink and Simscape can be used to develop custom SOH estimation algorithms aligned with specific requirements.
The white paper further covers cell balancing, battery charging, and protection. Simscape Battery provides prebuilt blocks for these functions, including support for CC-CV charging and monitoring of battery current, voltage, and temperature.
A key advantage of the model-based approach is the ability to validate designs before physical prototypes are available. Desktop simulation allows engineers to evaluate system architectures, test operational requirements, and compare control strategies. Real-time simulation can then extend validation through rapid prototyping and hardware-in-the-loop testing.
Hardware-in-the-loop testing provides a virtual battery environment in which BMS controllers can be tested against models representing the battery pack, loads, charger, contactors, and other system components. This allows teams to investigate difficult, expensive, or potentially destructive test cases without exposing costly battery prototypes to unnecessary risk.
The final stage focuses on hardware implementation and production code generation. Simulink can generate C/C++ and HDL code for microcontrollers, FPGAs, and ASIC implementations. The same algorithms validated through desktop simulation, rapid prototyping, HIL, and PIL can form the basis for production-ready software, reducing manual translation between engineering models and embedded implementation.
This white paper is designed for battery engineers, automotive engineers, BMS developers, controls engineers, electrification teams, energy storage developers, embedded software engineers, and organizations developing advanced battery-powered systems.
Download Developing Battery Systems with Simulink and Simscape from MathWorks to understand how model-based design can help accelerate battery development from cell and pack modeling through BMS validation, real-time testing, and production-ready implementation.
