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Keynote Address: HVDC Concept to Demonstrator Using Model-Based Design

Colin Davidson and Anthony Totterdell, Alstom Grid, and Andrew Bennett, MathWorks

Alstom Grid designs and manufactures electrical power system transmission equipment. In the power transmission industry, the trend is toward smart grids, renewable connections, and intelligent interconnections. High-voltage direct current (HVDC), in particular voltage source converter (VSC) technology, is increasingly prevalent in providing solutions for these systems.

In the design of VSC systems, effective verification and validation is paramount to achieve the complexity, quality, required reliability, and time-to-market targets. With this increased complexity and shorter time scales, the development process needs to evolve.

This presentation details how Alstom Grid adopted a Model-Based Design process, throughout a cross-functional team, to develop a new generation of VSC for HVDC transmission. Components, control and protection strategies, and overall system operation have been modelled, verified, and validated. The design has been deployed using automatic code generation to rapid prototyping and then production hardware.

The evolution to a Model-Based Design approach at Alstom Grid has enabled the rapid development of a complex system, increasing engineering productivity and achieving a production design in significantly less time than the existing design process.

Applying Model-Based Design to an On-Board Driver Support System for Economic Driving

Jonny Andersson, Scania

This presentation focuses on the development process for Scania Driver Support, an on-board support system developed for heavy-duty trucks that went into production in the fall of 2009. It is designed to detect and analyse driving situations whilst driving. It identifies scenarios in which the driver’s actions are especially important for the driving economy, and it gives advice on how to act just after a situation has been evaluated. Rating the driver’s behaviour is also a part of the method to positively influence driving style. Each situation is awarded up to five stars, and an average score also shows the overall progress.

Four categories define the criteria with which the driver is evaluated:

  • Hill driving – Encourages the driver to adapt the speed to the terrain for better fuel economy.
  • Brake use – Rewards smooth braking and encourages the use of auxiliary brakes for nonurgent braking.
  • Anticipation – Focuses on the interplay between acceleration and braking and encourages a defensive driving style.
  • Choice of gears – Gives advice on using the correct gears for fuel economy and performance.

Model-Based Design for Complex Systems: Tool and Workflow Strategies for Managing System-Level Designs

Rob Aberg, MathWorks

Modelling a system enables you to study and perform design-level control of complexity before committing to the expense of physical prototypes. But what makes a system complex? A straightforward observation is that design team size is first proportional to the work required to design new unique parts within a feasible time frame. Next, the role of part interdependencies in system complexity is assessed. This step benefits from creating a scalable project that separates part-level tasks from system-level tasks.

To achieve scalability, individual team members must be able to focus on their own work. Focus is created by partitioning the design into separately modelled components. Each component is allocated performance objectives and is versioned independently as the design progresses. Components are assembled into the system model using controlled interfaces and configurations.

Ideal model architectures minimise component interface size and interdependencies. This is done by selecting component sizes that match the focus of an individual engineer, who can then deliver a working component to the system level. At a higher level of abstraction, the concept of design variants allows for one model architecture to deliver a family of products using workflows that do not manually edit the system model itself.

Whether it is a design objective, such as system weight, power, or cost, or even a tool problem, such as simulation time or memory usage, the challenge is controlling component overages to prevent a cascade into system-level problems. This presentation includes examples of architecture selection, component and model management, and how to optimise simulation speed and model size within large designs.

Model-Based Design: Verification by Simulation

Dr. Marco Kunze, Continental Automotive

System Design Automation (SDA) is the established integrated environment for model-based function development (MBD) at Continental Automotive Group's Engine Systems Business Unit.

By running simulations on a PC, models can be verified at an early development step before using further enhanced and hardware-based methods such as rapid prototyping. These simulations are based on the requirements for the new or enhanced functionality to be realised. The bidirectional traceability from the requirements to the solution and its test is an important point for the complete tool and process landscape. But the central goal for function and software engineers working on these tasks is to get guidance through the huge number of functionalities offered in standard commercial tools.

We have therefore integrated the MBD test suite into the Simulink® based SDA environment. The central part of the test suite is the SDA Simulation Manager. This graphical user interface covers the generation of new test cases as well as the reuse of existing tests, their automated execution, and their documentation.

This presentation demonstrates the content of the MBD test suite as an XML-based test plan, specification, and report and shows how the model-based function development is supported by the SDA Simulation Manager, enabling users to drag and drop test cases from in-car measurements to the MATLAB® workspace or even to the Simulink model itself.

Using the SDA Simulation Manager and the MBD test suite optimises the efficiency of the daily tasks of the function development process by giving a direct relation between requirement engineering, test planning, and specification as well as test implementation and execution.

The integration in the standard development process enables the complete reuse of the modelling results in downstream activities in the V-cycle, such as production code generation, software module test, and documentation.

Modelling Transmission Systems with Simscape and SimDriveline

Rick Hyde, MathWorks

Developing a complete model of a driveline requires a combination of physics-based and data-driven modelling. A system-level driveline model can be used at an early stage in the development cycle to reduce risk and understand likely performance. This system-level model needs to be constructed from simple behavioural models of the main driveline components, and in a form that supports the making of design tradeoffs. Simscape provides an ideal platform for developing such models.

As the design cycle progresses, more detailed models are needed for some components. SimDriveline can be used to model effects such as friction losses, backlash, flexible dynamics, and locking/unlocking clutches. Other components, such as combustion engines, will require a data-driven approach to support higher-fidelity simulations. Simulink provides the necessary common platform for both of these modelling approaches, as well as support for hardware-in-the-loop testing.

The presentation features models of two vehicle transmission systems—one with a dual-clutch transmission, and the other with a hybrid transmission.

Agile Development of Drive and Power Systems

Dr. David Hinchley, Converteam

Converteam is a worldwide specialist in MW-scale power conversion engineering, providing customised solutions built around three core components: electrical machines, drives, and automation. Converteam addresses five main markets: marine (merchant and naval), industry, oil and gas, renewables (wind, wave, tidal, and solar), and traditional power generation.

This presentation uses specific examples to describe how MathWorks tools facilitate agile development of drive and power systems at Converteam UK. MATLAB and Simulink have been instrumental in the design of the integrated electric propulsion systems for the Type 45 Destroyer and Queen Elizabeth Class Aircraft Carriers. MathWorks tools have also helped drive the emergence of Converteam UK as a world player in the supply of power converters for wind turbines.

Converteam is actively working with MathWorks to further optimise the use of MATLAB and Simulink in the development process, reducing reliance on physical prototypes for test and verification and shortening time to market.

Model-Based Design in Development and Validation of Off-Highway Engine and Machine Control Systems

Dr. Peter Ladlow, Caterpillar

Caterpillar designs, develops, and manufactures a wide range of engines, machines, and generator sets. The level of complexity required in the electronic controls for these systems is expanding rapidly due to drivers such as meeting more stringent emissions legislation and improving product performance through better systems integration. Model-Based Design techniques are being utilised across the business as an enabler for increased efficiency in development processes. This presentation looks at how MathWorks tools are being used for analysis of plant performance, controller design verification, and controller optimisation through simulation and hardware-in-the-loop testing.

Techniques for Verification and Validation Throughout the Development Life Cycle

Mark Walker, MathWorks

Most people with experience in delivering control systems will recognise the benefit of discovering design constraints and implementation errors as early as possible. Using Model-Based Design, engineers can discover and fix these defects at an early stage. This presentation, supported by example models, illustrates the latest techniques and demonstrates the classes of defect that can be discovered.

Early in the design cycle, the effectiveness of activities such as performing closed-loop simulation, asserting expected behaviour, proving design properties, and demonstrating traceability to system requirements will be discussed. As implementation detail is captured, simulation, coverage measurement, test vector generation, and static checking play an increasing role. Once the implementation is realised in code, the benefits of run-time error checking, test reuse, and equivalence testing are considered.