Client Story

Upgrade to a modular, fully model-based drive control system

Customer

Manufacturer of
power converters

Industry

Renewable Energy

Migrating existing control software to a model-based design approach in MATLAB® Simulink® to create a scalable, future-proof development foundation

Challenge

Difficulty in maintaining and expanding system structures that have evolved over time

The existing system architecture was difficult to maintain, had limited scalability, and was heavily dependent on the expertise of individual specialists.

Increasing demands on software quality, security, and future-readiness

The goal was to transition the existing control software to a modern, model-based development approach, to sustainably improve software quality, and to create a scalable foundation for future developments.

Limited flexibility in variants and reusability

The existing system architecture was characterized by custom code modifications, low modularity, and a lack of consistency, which resulted in significant effort, for example, when developing new variants.

Solution

Replacing the historically evolved system architecture with a modular and fully model-based drive control system

Drawing on our many years of experience and proactive consulting, we supported the transformation of the existing software into a consistent model-based design approach using MATLAB® Simulink®. This solution resulted in clean, modular, and high-quality software.

Breaking down the control logic into smaller, independent models allows for flexible, requirement-specific distribution across different parts of the hardware. Because the algorithm is fully mapped in Simulink®—independent of the target hardware—the interaction of all models can be simulated within the development environment (MiL).

Our Simulink Target SIRIUS OS supports code generation for various target hardware and clean interface definitions for different communication channels (inter-core and AXI communication). In addition, automated CI pipelines perform analyses, tests, and new code generation whenever a model change is made. This ensures high software quality, and software changes can be tested directly on the hardware within just a few hours.

Result

Sustainable, maintainable, and modular software architecture

The clean, modular, and model-based inverter control system replaces the system architecture that was difficult to maintain and sustainably improves both the software quality and the future-proofing of the solution.

A scalable foundation for future systems and features

The fully model-based implementation makes it easier for interdisciplinary teams to understand, maintain, and further develop the software, thereby reducing reliance on individual experts.

Significantly shorter feedback cycles in development

Using the model-based approach, software changes can be developed, tested, verified, and integrated much more quickly.

Early error detection and improved debugging capabilities

Early simulation, model-in-the-loop testing, and automated testing help identify defects earlier and reduce project risks.

100% documentation coverage of the implementation

Transparency, compliance documentation, and traceability are significantly simplified throughout the entire product lifecycle.

Reduced error rate and quick startup

Simulation, automatic code generation, and static and dynamic analyses shorten feedback cycles and enable faster delivery of high-quality, reliable solutions.

Further customer projects

IIoT connectivity

IIoT connection of wind turbines

From time-consuming on-site maintenance to remote control and predictive maintenance

Web application

Process optimization through central Multi-project planning with Single point of truth

Proactive advice for individual
process optimization

HiL testing

Efficient remote tests without test bench conversion

dSPACE test bench with multiplexer for serial testing of three ECUs connected in parallel

Together we develop a solution that works.

Migrate existing systems to a modern, model-based development approach and manage increasing complexity in a sustainable manner.