Further reduction of fuel consumption and emissions is still the major driver for new powertrain developments and solutions. Electrification of powertrain systems is opening advanced possibilities. Brake recuperation systems and electronic clutches are state-of-the-art examples. Trends like automated and assisted driving as well as Car2x communication leverage new sources of information (e.g. radar, video, cloud). These new sources of information offer opportunities for new advanced powertrain features like energy-based predictive powertrain control. Additional sensors, multicore control units, and advanced powertrain topologies lead to an increased system complexity and make it difficult to develop an optimized control strategy. In order to master this increasing complexity with ever shortening development cycles, simulation and model-based development are key enablers for future business success.
Werner Quirant, Bosch
Ad hoc simulation is an instrumental first step to gain an understanding of a system under typical operating scenarios, yet it has become inadequate for verifying designs of increasing size and complexity. Simulink® verification products extend and complement simulation to provide additional rigor, automation, and insight that your designs are functionally correct, comply with standards, and are faithfully implemented on target hardware. This talk explains the vision and expanding capabilities of these tools for dynamic testing and formal methods–based static analysis. Bill also discusses how to apply these techniques systematically throughout a production development process to achieve higher quality and productivity.
Bill Aldrich, MathWorks
In the last two decades, the automotive software industry has undergone an enormous transformation from assembler hand-coding to model-based simulation with source code generation. With model-based development, the huge community of engineers gained the ability to create automotive software without knowledge of any programming language such as C or C++. On the other hand, from the perspective of Automotive SPICE, functional safety, and security, there are stringent requirements for specification of software detailed design regardless of whether the implementation is model based or not. The presentation shows how the software detailed design for model-based development can be structured to fulfill the requirements of ASPICE and ISO 26262 and bring sustainable benefits to the development organization and quality of the software product.
Dimitri Bermas, Volkswagen
Diego Barral, MathWorks
The concept of hybrid powertains incorporates two traction electric machines and a combustion engine as a range-extender combined with a parallel two-speed transmission. This configuration can also be classified as a parallel-series hybrid. It is characterized as high efficiency with good driving performance paired with a high level of comfort, and a high degree of system integration evident through simulation results. This concept will be implemented on a test bench and a demo car as a proof of concept. The complete powertrain control software was built from scratch in MATLAB® and Simulink®.
This presentation gives an overview of the software structure being used as well as how some of the initial challenges of the software concept relating to modularity, reusability and adaptability were resolved. The presentation also includes a powertrain monitoring und safety concept that was developed using Simscape™.
Mubin Bhai, MBtech
In recent decades, hybrid and electric vehicle powertrains have emerged in the automotive market and proved to be among the most viable alternatives for reducing emissions and fossil fuel usage in transportation. Since then, the vehicle electrification scenario has initiated a boost in the development of many cutting-edge solutions, such as switched reluctance (SR) motors, which appear as a great technology for automotive electrification as they are free from rare earth metals and a very cost-effective solution. This session presents the evolutions on a SR motor control algorithm enabled by using Model-Based Design from the conceptual phase down to the implementation on Zynq®-7000 SoC platform.
Steven Bervoets, Punch Powertrain
The ultimate goal of student race team InMotion is to design, build, and race their series-hybrid endurance racer, the IM01, during the 24 hours of Le Mans. On the road to Le Mans, InMotion will first attempt to break several lap records with the full-electric KP&T IM/e. To develop and test both the KP&T IM/e and the IM01, InMotion is using MATLAB® and Simulink® to speed up the development process. This presentation portrays an overview of their work.
Johan van Uden, InMotion
IMPROVE (Integration and Management of Performance and Road Efficiency of Electric Vehicle Electronics) is a project under EU Green Cars Initiative Objective GC-ICT-2013.6.7 Electro-mobility. The project consortium aims to develop an electric light commercial vehicle (LCV) for fleet applications that is enhanced with cloud connected functionalities. The consortium consists of 10 partners from 8 countries. Within this project, a demo vehicle (FIAT Doblo) is developed as an electric vehicle with an on-board telematics unit for cloud connection. Because the partners were in different countries and had different methodologies for software development, a common ground needed to be set for concurrent software development in Simulink®. By using Model-Based Design and the embedded coding features in Simulink, the software development process was significantly simplified. It also simplified the concurrency of the software development work among the different partners. This presentation gives an overview about the use of Model-Based Design as part of the IMPROVE project.
Utku Karakaya, TOFAS
Electronic control units (ECUs) for advanced driver assistance systems strongly rely on sensor data to interpret a complex environment and traffic situations. Today development and testing of such ECUs is mostly supported by simulations applying a virtual vehicle approach in several steps from MIL via SIL to HIL. Since there are countless different traffic situations, simulation of such scenarios can save time and effort over real-world testing while providing better test coverage. During the development and testing of the corresponding algorithms it is important to provide realistic sensor signals for the simulation in real time. In this session, we present an approach for parallelization of the computing processes that enables model equations to be solved more quickly. The overall vehicle model and the ECU algorithm are simulated in Simulink®. For the physical sensor simulation, the native GPU of a PC is used. We also present benchmark results from development of a parking assist system that uses ultrasonic sensors in a parallel parking scenario. The simulation is executed on a Simulink Real-Time™ and Speedgoat HIL system.
Dr. Frederic Chucholowski, Tesis Dynaware
Model-Based Design in Simulink® is widely used for developing application software components. Model-Based Design has proven to be a good methodology to deal with complexity. On the other hand, for basic software modules, manual coding is widely used. However, with the introduction of AUTOSAR (AUTomotive Open System ARchitecture), basic software modules are becoming more standardized yet more complex. In this session, we present our approach to use Model-Based Design and its advantages to implement AUTOSAR basic software modules.
Mohamed Soliman and Amjad Elshenawy, Valeo
Diesel engines are becoming extremely popular across various parts of the world due to their good fuel economy. In Germany, the biggest European market, diesel-powered vehicles enjoy good market share. The ability to maximize power and fuel economy by optimizing spark timing for a given air/fuel ratio is limited by the engine knock that occurs when the temperature or pressure in the unburned air/fuel mixture (end gases) exceeds a critical level, causing auto-ignition of the end gases. Persistent knock can cause severe damage to pistons and exhaust valves. More importantly, many automotive customers prefer vehicles with little diesel knocking. This presentation discusses how to use data analysis techniques in MATLAB® to optimize diesel engines applying the diesel engine knocking index.
Sathvik Tarikere Sathyanarayana, Bosch
The increasing efforts of the automotive industry to connect the vehicle with other traffic participants as well as the infrastructure surrounding the car will be a great step in enabling new automotive innovations. However, this also comes with an additional effort that the automotive industry has to manage. They must make sure that possible-cyber security attacks do not lead to hazardous driving situations due to weaknesses in the vehicle electrical system. These hazardous driving situations can occur when security vulnerabilities enable hackers access to safety-critical vehicle functions. Tradeoff decisions need to be carefully considered to fulfill requirements for functionality, performance, safety, and security, while supporting standards such as ISO 26262, MISRA-C, or CERT-C. These requirements may sometimes even cause conflicting situations when it comes to design decisions. In this presentation, Stefan discusses methods that help engineers to identify vulnerabilities, comply with standards, and make informed design decisions.
Tanvir Hussein and Stefan David, MathWorks
A major focus of predictive maintenance is intelligent monitoring of engines to avoid future failures. Using real-world data, this talk shows you how to use machine learning techniques in MATLAB® to identify anomalies and predict future engine health. Learn how MATLAB is used to build prognostics algorithms and take them into production, enabling automotive companies to improve the reliability of products and build new predictive maintenance services.
Dmitrij Martynenko, MathWorks
Model-Based Design affords many advantages over traditional development by offering high-level design abstractions and automatic generation of production code. Modeling and code generation for AUTOSAR software components is especially beneficial because specifying and synchronizing lengthy identifiers in designs, code, and description files is difficult for humans, but easily automated by tools. In recent releases, MATLAB® and Simulink® products for Model-Based Design offer wide-ranging and deep support for AUTOSAR.
This master class is intended for systems and software engineers who wish to understand the basic concepts and best approaches for using Simulink for AUTOSAR design and Embedded Coder® for software implementation. Michael provides a brief overview of the AUTOSAR standard and then dives into product demonstrations showing how you can use Simulink and Embedded Coder to design, simulate, verify, and generate code for AUTOSAR application software components.
Through product demonstrations, you’ll learn about:
- The Simulink approach to AUTOSAR
- Modeling styles
- AUTOSAR design workflows
Michael Fröstl, MathWorks
With autonomous driving, the automotive industry is entering a new era of mobility. Accomplishing this requires overcoming several new technology development challenges for future vehicle systems. This master class shows how to use MATLAB® and Simulink® to develop ADAS and autonomous driving applications including component and system-level development and simulation to facilitate early verification. Example applications for camera, radar, and lidar-based sensor systems including automated ground-truth labeling solutions plus sensor fusion techniques are explained.
Marco Roggero, MathWorks
This talk outlines verification and validation methods in Model-Based Design and discusses the applicability and impact of each method at the model, code, and integration level. The presented methods range from ensuring traceability, modeling and MISRA-C guidelines checking, measuring test coverage, test automation, and test generation to formal methods to formally verify functional correctness as well as the absence of run-time errors. For compliance with ISO 26262-6, a coherent workflow is suggested along with considerations on tool qualification (ISO 26262-8).
Marc Segelken, MathWorks