Emerging Automotive Technologies

Electric powertrains are estimated to propel a large part of road vehicles in the future, due to their high efficiency and zero tailpipe emissions. But, the cost and weight of batteries and the time to charge them are arguments for the conventional powertrain in many vehicles. This makes it important for engineers working with vehicles to understand how both these powertrains work, and how to determine their performance and energy consumption for different type of vehicles and different ways of driving vehicles.

This course is aimed at learners with a bachelor's degree or engineers in the automotive industry who need to develop their knowledge about electric powertrains.

In this course, you will learn how electric and conventional combustion engine powertrains are built and how they work. You will learn methods to calculate their performance and energy consumption and how to simulate them in different driving cycles. You will also learn about the basic function, the main limits and the losses of:

• Combustion engines,
• Transmissions
• Electric machines,
• Power electronics
• Batteries.

This knowledge will also be a base for understanding and analysing different types of hybrid vehicles, discussed in the course, Hybrid Vehicles.
As a result of support from MathWorks, students will be granted access to MATLAB/Simulink for the duration of the course.

Why are hybrid vehicles still more common than battery electric ones? Why are electric vehicles still more expensive than conventional or hybrid ones? In this course, you will get the answers to this and much more.

While electric motors can improve vehicles regarding performance, energy consumption and emissions, they suffer from high cost and weight of batteries. Smart combinations of electric motors and combustion engines in a hybrid powertrain can combine these strengths with the advantages of combustion engines.

This course is aimed at learners with a bachelor's degree or engineers in the automotive industry who need to develop their knowledge about hybridpowertrains.

Inthis course, we willexamine different mechanical layouts of hybrid powertrains and how they influence the performance and complexity of the powertrain. Different sizing of powertrains in micro, mild, full hybrids, as well as plug-in hybrids, is also discussed and you'll learn how they can be modelled and analyzed for example by simulation of driving cycles. You will also learn about the Energy Management system and how this controls the hybrid powertrain modes and when to charge and discharge the battery.

As a result of support from MathWorks, students will be granted access to MATLAB/Simulink for the duration of the course.

Engineers in the automotive industry are required to understand basic safety concepts. With increasing worldwide efforts to develop connected and self-driving vehicles, traffic safety is facing huge new challenges. This course is for students or professionals who have a bachelor's degree in mechanical engineering or similar and who are interested in a future in the vehicle industry or in road design and traffic engineering. It's also of value for people already working in these areas who wantbetter insight into safety issues.

This course teaches the fundamentals of active safety (systems for avoiding crashes or reducing crash consequences) as well as passive safety (systems for avoiding or reducing injuries). Key concepts include in-crash protective systems, collision avoidance, and safe automated driving. The course will introduce scientific and engineering methodologies that are used in the development and assessment of traffic safety and vehicle safety. This includes methods to study the different components of real-world traffic systems with the goal to identify and understand safety problems and hazards. It includes methods to investigate the attitudes and behavior of drivers and other road users as well as recent solutions to improve active safety. Italso includes methods to study human body tolerance to impact and solutions to minimize the injury risk in crashes.

Study topics include crash data analysis and in-situ observational studies of drivers and other road users by the use of instrumented vehicles and roadside camera systems. Solutions in active safety, such as driver alertness monitoring, driver information as well as collision avoidance and collision mitigation systems, will be described. Examples of in-crash protective systems are combinations of traditional restraints such as seat belts and airbags but with advanced functions such as automatic adaption to the individual occupant as well as pre-collision activation based on advanced integrated sensor systems and communication systems.

The course will be based on recorded lectures that use videos and animations to enhance the experience. Online tutorials that access simulation models will give the participants an experience of influencing parameters in active safety and passive safety systems.

As a result of support from MathWorks, students will be granted access to MATLAB/Simulink for the duration of the course.

Modeling, control design, and simulation are important tools supporting engineers in the development of automotive systems, from the early study of system concepts (when the system possibly does not exist yet) to optimization of system performance. This course provides a theoretical basis to model-based control design with the focus on systematically develop mathematical models from basic physical laws and to use them in control design process with specific focus on automotive applications.

You will learn the basics of mathematical modeling applied to automotive systems, and based on the modeling framework different type of controller and state estimation methods will be introduced and applied. Starting from a pure state-feedback concept down to optimal control methods, with special attention on different automotive applications. Different methods for state reconstruction is also introduced and discussed in the course. Exercises play an important rolethroughout the course.

This course is aimed at learners with a bachelor's degree or engineers in the automotive industry who need to learn more about mathematical modelling of automotive systems.

In this course, we will introduce you to the fundamentals of sensor fusion for automotive systems. Key concepts involve Bayesian statistics and how to recursively estimate parameters of interest using a range of different sensors.

The course is designed for students who seek to gain a solid understanding of Bayesian statistics and how to use it to fuse information from different sensors. We emphasize object positioning problems, but the studied techniques are applicable much more generally. The course contains a series of videos, quizzes and hand-on assignments where you get to implement many of the key techniques and build your own sensor fusion toolbox.

The course is self-contained, but we highly recommend that you also take the course ChM015x: Multi-target Tracking for Automotive Systems. Together, these courses give you an excellent foundation to tackle advanced problems related to perceiving the traffic situation around an autonomous vehicle using observations from a variety of different sensors, such as, radar, lidar and camera.

Autonomous vehicles, such as self-driving cars, rely critically on an accurate perception of their environment.

In this course, we will teach you the fundamentals of multi-object tracking for automotive systems. Key components include the description and understanding of common sensors and motion models, principles underlying filters that can handle varying number of objects, and a selection of the main multi-object tracking (MOT) filters.

The course builds and expands on concepts and ideas introduced in CHM013x: "Sensor fusion and nonlinear filtering for automotive systems". In particular, we study how to localize an unknown number of objects, which implies various interesting challenges. We focus on cameras, laser scanners and radar sensors, which are all commonly used in vehicles, and emphasize on situations where we seek to track nearby pedestrians and vehicles. Still, most of the involved methods are more general and can be used for surveillance or to track, e.g., biological cells, sports athletes or space debris.

The course contains a series of videos, quizzes and hands-on assignments where you get to implement several of the most important algorithms.

Learn from award-winning and passionate teachers to enhanceyour knowledge at the forefront of research on self-driving vehicles. Chalmers is among the top engineering schools that distinguish itself through its close collaboration with industry.