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This course, designed for EV motor engineers and graduate participants, systematically introduces EV motor design analysis and test verification. Combined with engineering practice, it discusses typical EV motor design cases and practical issues related to EV motor technology, aiming to broaden the horizon of EV motor design engineers and improve their problem-solving skills.

This training class provides a comprehensive overview of the principles, processes, and objectives of model testing – from requirements to model tests. We offer step-by-step guidance from creating requirements-based test specifications, through testing TargetLink and/or Embedded Coder models, to automated test evaluation based on test assessments and back-to-back/regression tests. In particular, we will emphasize ISO 26262-compliant test management and explain the test process for MiL and SiL, as well as tracing requirements to test specifications and test assessments.

This course is offered in China only and presented in Mandarin Chinese. The course materials are bilingual (English and Chinese). Energy conservation and emission reduction has always been a goal for many countries. Especially since the introduction of China VI Vehicle Emission Standards and CAFC & NEV Credit Regulation, the output of NEVs has grown explosively while accompanied by a large number of car fire burning problems and complaints about the shortening of proclaimed electric-only driving range.

Vehicle cybersecurity vulnerabilities could impact a vehicle's safe operation. Therefore, engineers should ensure that systems are designed free of unreasonable risks to motor vehicle safety, including those that may result due to existence of potential cybersecurity vulnerabilities. The automotive industry is making vehicle cybersecurity an organizational priority.

Getting the strength and hardness you need in your steel requires knowledge of the effects of alloy composition and heat treating on steel properties. With knowledge of steel metallurgy you’ll be better able to: Select the most appropriate alloy and heat treating process for your application Evaluate suppliers Develop manufacturing processes Solve quality problems A good understanding of what can be done to modify the properties of steel is critical for being more productive and profitable.

Ferrous metals contain iron and are prized for their tensile strength and durability. Most are magnetic and contain a high carbon content which generally makes them, with the exception of wrought iron and stainless steel, vulnerable to rust. The following seven eLearning courses are included in the Ferrous Materials Bundle: Steel and Cast Iron. Each course is approximately one-hour in duration. Modules include: Introduction to Physical Properties, Introduction to Mechanical Properties, Introduction to Metals, Hardness Testing, Ferrous Metals, Classification of Steel, Essentials of Heat Treatment of Steel.

Finite Element Analysis (FEA) is a powerful and well recognized tool used in the analysis of heat transfer problems. However, FEA can only analyze solid bodies and, by necessity thermal analysis with FEA is limited to conductive heat transfer. The other two types of heat transfer: convection and radiation must by approximated by boundary conditions. Modeling all three mechanisms of heat transfer without arbitrary assumption requires a combined use of FEA and Computational Fluid Dynamics (CFD).

Introduction to Power Electronic Converters: In this course, you will learn why we need power converters. We will discuss the basic principles of power conversion and you will receive a brief overview of the various types of power converters. You will learn how to recognize different voltage, current, and power levels, as well as the AC or DC character of electrical power, through examples of common, present-day electrical applications. Power conversion and efficiency in battery systems: In this course, you will be taken into the lab to get acquainted with power conversion principles and efficiency measurements.

Design of Experiments (DOE) is a methodology that can be effective for general problem solving, as well as for improving or optimizing product design and manufacturing processes. Specific applications of DOE include, but are not limited to, identifying root causes to quality or production problems, identifying optimized design and process settings, achieving robust designs, and generating predictive math models that describe physical system behavior. This introductory eLearning course provides an example scenario to give learners the opportunity to discover situations that may warrant a designed experiment.

Engineers and managers involved with product development are continually challenged to reduce time to market, minimize warranty costs, and increase product quality. This results in less time for testing, which means the need for effective accelerated test procedures has never been greater. Accelerating your testing program sounds great, but how do you begin? This course starts by taking a look at statistical models for reliability testing. It looks specifically at how sample size and testing timeframe affect what you’re able to do versus what you'd ideally do with more time and money.

This course discusses different methods for battery testing. The importance of testing, related standards, needed testing infrastructure and analysis tools are individually tackled. This series also highlights how the output of battery testing is viable for battery modelling that supports the battery use phase and can possibly be implemented on battery management systems.