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MOOC
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.
- Subjects:
- Electrical Engineering, Mechanical Engineering, and Transportation
- Keywords:
- Electric vehicles Automobiles -- Power trains
- Resource Type:
- MOOC
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Others
Distinguish yourself by learning to work with text data.
- Subjects:
- Computing
- Keywords:
- Python (Computer program language) Natural language processing (Computer science)
- Resource Type:
- Others
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Others
Use TensorFlow to take machine learning to the next level. Your new skills will amaze you.
- Subjects:
- Computing
- Keywords:
- Python (Computer program language) Machine learning
- Resource Type:
- Others
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Others
Discover the most effective way to improve your models.
- Subjects:
- Computing
- Keywords:
- Machine learning Data mining Python (Computer program language)
- Resource Type:
- Others
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Others
Solve short hands-on challenges to perfect your data manipulation skills.
- Subjects:
- Computing
- Keywords:
- Python (Computer program language) Electronic data processing Information visualization
- Resource Type:
- Others
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MOOC
The building industry is exploding with data sources that impact the energy performance of the built environment and health and well-being of occupants. Spreadsheets just don’t cut it anymore as the sole analytics tool for professionals in this field. Participating in mainstream data science courses might provide skills such as programming and statistics, however the applied context to buildings is missing, which is the most important part for beginners. This course focuses on the development of data science skills for professionals specifically in the built environment sector. It targets architects, engineers, construction and facilities managers with little or no previous programming experience. An introduction to data science skills is given in the context of the building life cycle phases. Participants will use large, open data sets from the design, construction, and operations of buildings to learn and practice data science techniques. Essentially this course is designed to add new tools and skills to supplement spreadsheets. Major technical topics include data loading, processing, visualization, and basic machine learning using the Python programming language, the Pandas data analytics and sci-kit learn machine learning libraries, and the web-based Colaboratory environment. In addition, the course will provide numerous learning paths for various built environment-related tasks to facilitate further growth.
- Keywords:
- City planning -- Statistical methods Python (Computer program language) Information visualization
- Resource Type:
- MOOC
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MOOC
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.
- Subjects:
- Electrical Engineering, Mechanical Engineering, and Transportation
- Keywords:
- Automobiles -- Electronic equipment Automotive sensors
- Resource Type:
- MOOC
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MOOC
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.
- Subjects:
- Electrical Engineering, Mechanical Engineering, and Transportation
- Keywords:
- Electric vehicles Hybrid electric vehicles
- Resource Type:
- MOOC
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MOOC
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.
- Subjects:
- Electrical Engineering, Mechanical Engineering, and Transportation
- Keywords:
- Automobiles -- Design construction Computer vision Automated vehicles
- Resource Type:
- MOOC
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Courseware
Are you an engineer, scientist or technician? Are you dealing with measurements or big data, but are you unsure about how to proceed? This is the course that teaches you how to find the best estimates of the unknown parameters from noisy observations. You will also learn how to assess the quality of your results. TU Delft’s approach to observation theory is world leading and based on decades of experience in research and teaching in geodesy and the wider geosciences. The theory, however, can be applied to all the engineering sciences where measurements are used to estimate unknown parameters. The course introduces a standardized approach for parameter estimation, using a functional model (relating the observations to the unknown parameters) and a stochastic model (describing the quality of the observations). Using the concepts of least squares and best linear unbiased estimation (BLUE), parameters are estimated and analyzed in terms of precision and significance. The course ends with the concept of overall model test, to check the validity of the parameter estimation results using hypothesis testing. Emphasis is given to develop a standardized way to deal with estimation problems. Most of the course effort will be on examples and exercises from different engineering disciplines, especially in the domain of Earth Sciences. This course is aimed towards Engineering and Earth Sciences students at Bachelor’s, Master’s and postgraduate level.
- Keywords:
- Observers (Control theory) Mathematical statistics
- Resource Type:
- Courseware