E-Vehicle Electronic

OBJECTIVES

The objective of this training program is to provide participants with a comprehensive understanding of communication protocols in electric vehicles (EVs), specifically Controller Area Network (CAN) and Local Interconnect Network (LIN) protocols. Participants will learn about the basics of EV communication, the principles of CAN and LIN protocols, their applications, and their integration into EV systems. The training program will also cover practical aspects such as diagnostics, troubleshooting, and network analysis in the context of EV communication.

OUTCOMES

• Understand the fundamentals of communication protocols in EVs, including CAN and LIN.
• Explain the principles of CAN and LIN protocols, their architecture, and how they are used in EV systems.
• Analyze and diagnose communication issues in EVs using CAN and LIN tools and techniques.
• Design and implement communication interfaces and networks in EV systems using CAN and LIN protocols.
• Troubleshoot and resolve common communication problems in EVs, including network conflicts, bus-off conditions, and error handling.
• Apply best practices for integrating CAN and LIN communication into EV systems to ensure reliable and efficient data exchange.

SCOPE

• EV System Engineer: Participants can work as system engineers responsible for designing, developing, and integrating communication networks in EV systems. They can ensure smooth communication between various subsystems, such as powertrain, battery management, and charging systems, using CAN and LIN protocols.
• EV Diagnostics Technician: Participants can work as technicians responsible for diagnosing and troubleshooting communication issues in EVs using CAN and LIN tools and techniques. They can analyze network data, identify problems, and implement solutions to ensure proper communication between EV components.
• EV Testing and Validation Engineer: Participants can work as testing and validation engineers responsible for testing and validating EV communication systems using CAN and LIN protocols. They can conduct network analysis, perform simulations, and verify the performance and reliability of communication interfaces in EVs.
• EV Service Technician: Participants can work as service technicians responsible for maintaining and repairing EV communication systems using CAN and LIN protocols. They can diagnose and resolve communication issues, update firmware, and ensure proper functioning of communication networks in EVs.

PROJECTS

• Design and implementation of a communication network for an electric vehicle, including CAN and LIN protocols, to facilitate data exchange between different EV subsystems.
• Diagnosing and troubleshooting communication issues in an electric vehicle using CAN and LIN tools and techniques, and implementing solutions to resolve the problems.
• Testing and validation of an EV communication system using CAN and LIN protocols, including performance testing, reliability testing, and compliance testing.
• Integration of third-party components, such as sensors or actuators, into an existing EV communication network using CAN and LIN protocols, and verifying their functionality.
• Development of a communication interface or gateway for an EV system to enable communication between different communication protocols, such as CAN and LIN, or between EV and external systems

OBJECTIVES

The objective of this training program is to provide participants with comprehensive knowledge and skills related to E-Vehicle Connectivity and Radar Technology. Participants will learn about the principles, concepts, and applications of radar technology, with a focus on 77 GHz and 24 GHz radar systems. They will also gain an understanding of E-Vehicle connectivity, including the integration of radar systems into E-Vehicle platforms, and tracking system technologies.

OUTCOMES

• Understand the basic principles and concepts of radar technology, including radar wave propagation, radar components, and radar system architectures.
• Gain knowledge about E-Vehicle connectivity and the integration of radar systems into E-Vehicle platforms for various applications, such as advanced driver assistance systems (ADAS) and autonomous driving.
• Acquire skills in designing, testing, and troubleshooting radar systems, including the selection and configuration of radar sensors, radar signal processing, and radar data interpretation.
• Understand the different frequencies used in radar technology, with a focus on 77 GHz and 24 GHz radar systems, and their respective applications.
• Gain knowledge about tracking system technologies, including object detection, tracking, and fusion, and their applications in various industries, such as automotive, aerospace, and defense.

SCOPE

• Radar System Engineer
• E-Vehicle Connectivity Engineer
• ADAS Engineer
• Autonomous Vehicle Engineer
• Radar Signal Processing Engineer
• Radar Application Engineer
• Tracking System Engineer
• Radar Testing and Validation Engineer

PROJECTS

• Design and simulation of a radar system using 77 GHz or 24 GHz radar sensors, including antenna selection, radar signal processing, and data interpretation.
• Integration of radar systems into an E-Vehicle platform, including sensor placement, communication protocols, and data fusion techniques.
• Development of a tracking system using radar technology for object detection, tracking, and fusion in a specific industry application, such as aerospace or defense.
• Testing and validation of radar systems, including performance evaluation, signal analysis, and troubleshooting of radar system issues.

Objectives

• Knowledge Acquisition: Equip participants with fundamental knowledge of ADAS technologies, including sensors, algorithms, and system architecture.
• Skill Development: Develop hands-on skills in the design, testing, and implementation of basic ADAS features such as lane-keeping assistance, adaptive cruise control, and automated braking systems.
• Industry Standards Familiarization: Ensure participants understand the regulatory and safety standards related to ADAS, such as ISO 26262 (Functional Safety).
• Innovation Encouragement: Foster creativity and innovation by encouraging participants to explore new ADAS concepts or improve existing systems.

Outcomes

• Competency in ADAS: Participants will demonstrate a solid understanding of entry-level ADAS technologies and their applications.
• Practical Skills: Participants will complete hands-on projects that involve the development or simulation of ADAS components, showcasing their ability to apply theoretical knowledge.
• Certification: Award participants with a certification of completion, signifying their readiness for roles related to ADAS development in the automotive industry.
• Portfolio Development: Participants will have a portfolio of completed projects that they can present to potential employers, highlighting their practical skills and knowledge.

Scope

• Target Audience: Entry-level engineers, recent graduates, or professionals transitioning into the automotive sector.
• Duration: 8-12 weeks, with a mix of theoretical and practical sessions.
• Content Coverage:
  • Introduction to ADAS and its components (sensors, actuators, control units).
  • Basics of computer vision and sensor fusion.
  • Introduction to key ADAS features (e.g., lane departure warning, adaptive cruise control).
  • Regulatory standards and safety protocols.
  • Tools and software commonly used in ADAS development (e.g., MATLAB, Simulink, ROS).
• Delivery Mode: A combination of online and offline sessions, with practical labs and projects conducted in collaboration with industry partners.

Project

• Title: “Design and Implementation of a Basic Lane-Keeping Assistance System”
• Description:
  • Participants will work on a project to design and implement a lane-keeping assistance system, a fundamental ADAS feature. The project will involve:
    • Designing the system architecture.
    • Simulating the system using tools like MATLAB/Simulink.
    • Implementing the system on a hardware platform (e.g., Raspberry Pi with camera module).
    • Testing and validating the system using predefined test cases.
  • Expected Deliverables:
    • System design documents.
    • Simulation results.
    • Working prototype of the lane-keeping system.
    • A final report and presentation summarizing the project.

Objectives

• Deep Technical Mastery: Develop a thorough understanding of advanced ADAS technologies, including machine learning, sensor fusion, and real-time data processing.
• System Integration Skills: Enable participants to design, integrate, and optimize complex ADAS systems across various platforms and vehicles.
• Innovation and Research: Encourage participants to engage in cutting-edge research and innovation, contributing to the development of next-generation ADAS technologies.
• Compliance with Industry Standards: Ensure participants are proficient in adhering to the latest automotive safety and regulatory standards, including cybersecurity considerations in ADAS.

Outcomes

• Expert-Level Competency: Participants will demonstrate advanced skills in developing, testing, and optimizing complex ADAS systems.
• Innovative Solutions: Participants will produce innovative solutions or enhancements to existing ADAS technologies, potentially leading to patentable ideas or research publications.
• Leadership in ADAS Projects: Participants will be prepared to lead ADAS development projects, manage cross-functional teams, and integrate systems across multiple vehicle platforms.
• Advanced Certification: Participants will receive an advanced certification, recognized by industry leaders, indicating their high-level expertise in ADAS technologies.

Scope

• Target Audience: Experienced engineers, automotive professionals, and researchers with foundational knowledge in ADAS or related fields.
• Duration: 16-24 weeks, with intensive hands-on labs, research projects, and industry collaboration.
• Content Coverage:
  • Advanced Sensor Technologies: Deep dive into LiDAR, RADAR, and advanced camera systems.
  • Machine Learning in ADAS: Implementation of machine learning models for object detection, classification, and decision-making.
  • Sensor Fusion and Data Integration: Techniques for combining data from multiple sensors to create a cohesive system view.
  • Real-Time Processing: Strategies for real-time data processing and decision-making in ADAS.
  • Cybersecurity in ADAS: Protecting ADAS systems against cyber threats and ensuring data privacy.
  • Regulatory Compliance: Advanced understanding of global automotive safety standards and their application to ADAS.
• Delivery Mode: A blend of online lectures, on-site labs, workshops, and industry-driven projects, with a strong focus on practical application.

Project

• Title: “Development of a Fully Autonomous Parking System with Cybersecurity Features”
• Description:
  • Participants will develop a fully autonomous parking system, which is an advanced ADAS feature, incorporating cybersecurity measures. The project will involve:
    • Designing the complete system architecture, including sensor layout, data processing units, and control algorithms.
    • Implementing machine learning models for object detection and path planning.
    • Integrating multiple sensors (e.g., LiDAR, RADAR, and cameras) for accurate environmental perception.
    • Ensuring real-time data processing and decision-making.
    • Incorporating cybersecurity protocols to protect the system from potential threats.
    • Testing the system in a simulated environment, followed by real-world trials.
  • Expected Deliverables:
    • Detailed design documents and system architecture.
    • Source code for machine learning models and control algorithms.
    • Simulation results demonstrating system functionality.
    • A functional prototype tested in both simulated and real environments.
    • A comprehensive report and presentation detailing the project, including a cybersecurity analysis.

OBJECTIVES

The objective of this training program is to provide participants with a comprehensive understanding of the electronic applications in an electric vehicle (EV) powertrain system. The program will cover various aspects such as battery selection, motor sizing, thermal management system, body control module (BCM), and battery management system (BMS). Participants will gain practical knowledge through hands-on projects and calculations related to EV powertrain electronics.

OUTCOMES

• Understand the principles of EV powertrain electronics and their applications.
• Calculate and select the appropriate battery for an EV based on factors such as voltage, WLTP (Worldwide Harmonized Light Vehicle Test Procedure) requirements, capacity, and configuration.
• Size and select the appropriate type of motor for an EV based on performance requirements and calculations.
• Design and implement thermal management systems for an EV powertrain to ensure efficient operation.
• Understand the role and functionality of body control modules (BCMs) in an EV powertrain system.
• Understand the importance of battery management systems (BMS) in an EV and their functions.

SCOPE

• EV Powertrain Electronics Engineer: Responsible for designing, developing, and testing electronic systems and components in an EV powertrain, including battery selection, motor sizing, thermal management systems, BCM, and BMS.
• EV Systems Integration Engineer: Responsible for integrating various electronic systems in an EV powertrain, ensuring their proper functionality and performance.
• EV Testing and Validation Engineer: Responsible for testing and validating EV powertrain electronics components and systems to ensure compliance with regulatory standards and performance requirements.
• EV Powertrain Consultant: Providing expert advice and guidance to EV manufacturers or organizations in the selection, design, and implementation of powertrain electronics for EVs.

PROJECTS

• Battery Selection Project: Participants will calculate and select an appropriate battery for an EV based on factors such as voltage, WLTP requirements, capacity, and configuration.
• Motor Sizing Project: Participants will size and select the appropriate type of motor for an EV based on performance requirements and calculations.
• Thermal Management System Project: Participants will design and implement a thermal management system for an EV powertrain to ensure efficient operation.
• BCM and BMS Project: Participants will understand the functionality of BCM and BMS in an EV powertrain system and design appropriate systems for effective control and management of the powertrain electronics.