Embedded Systems for Software defined Vehicles (SDV ES) - Cohort2
Indian Institute of Technology Madras
About Programme
It’s an acknowledged fact that vehicles have become computers on wheels or even network of computers on wheels and with Software Defined Vehicles they are going the way of mobile phones – mobility will only be one of their functions. Software has become critical in determining the success of a product and at the same time, problems associated with the software have also grown. Numerous studies report that traditional OEMs are way behind startups such as Tesla, Nio, Xiaomi and Xpeng. Gartner Digital Automaker Index 2024 says legacy OEMs from 2023 to 2024 have made very little progress on their potential to fully monetize software unlike Chinese OEMs and new market entrants. If the trend continues, traditional OEMs risk becoming low margin hardware suppliers while software providers corner the larger pie of the Software Defined Vehicles.
Mode:
30 hours of recorded videos and 18 hours (9weeks - 2hrs each week) of online live interactive sessions with the faculty.
Programme Content
Module Description:
Historical overview: Evolution of automotive embedded systems;incremental development vs.burn and rebuild.
Concepts Covered:
Mobility and automobiles.
Digital Controllers and their evolution.
Vehicle domains and networks.
Features of a typical SDV.
What makes automotive embedded systems challenging?
Differences between Software Engineering and Traditional Automotive Engineering.
Analysis of some software recalls.
Learning Outcomes:
Problems with current architecture and development methods
Applications:
Appreciation for clean-slate thinking
Module Description:
Brief introduction to the structured development model ensuring traceability and reliability across automotive system life cycles.
Concepts Covered:
System Life Cycle
V-Process
Separation of Problem and Solution Domains
Requirements: Functional, Logical and Physical Description / Design
Industry Requirements – Safety, Performance, Reliability, Durability, Cost
Regulatory Requirements
Model-Based Systems Engineering
Traceability
SysML Diagrams
SysML Modelling Tools
Learning Outcomes:
Separation of problem and solution domains .
Iterative nature of development
Applications:
Structured approach to embedded system design
Module Description:
Covers the end-to-end process of developing automotive embedded systems.
Concepts Covered:
Process Approach
IATF 16949
Software Development Processes – Waterfall, Iterative, Agile and DevOps
Automotive SPICE
Tools for Life Cycle Management – PLM and ALM
FMEA
Functional Safety – ISO 26262, FMEDA
Cyber Security – UN R155/R156, ISO 21434/24089, AIS 189/190
Learning Outcomes:
Manage iterative development and prototyping phases successfully.
Functional Safety and Cyber Security.
Applications:
Compliance documentation for vehicle platforms.
Module Description:
Explores the architecture and hardware of automotive Electronic Control Units (ECUs) and their integration into vehicle networks.
Concepts Covered:
System Architecture
Hardware Architecture
Microprocessors
Microcontrollers and System on Chip (SoC)
Peripherals: GPIO, Timers, ADC, DAC
Memory Types: Flash, RAM, EEPROM
ADCs and DACs
PCB
Communication Networks and Protocols (CAN, Ethernet)
OTA (Over-the-Air Updates)
Learning Outcomes:
Distinguish between major alternatives for h/w and networks.
Applications:
H/W selection
Module Description:
Addresses foundational and advanced control theory as applied in automotive embedded systems, focusing on real and virtual vehicle dynamics,feedback control, and system stabili.
Concepts Covered:
Classical and Modern Control Theory
Frequency and Time Domain Analysis
SISO and MIMO Systems
Observability, Controllability and Stability
Open and Closed Loop Systems
Control Methods: PID, Sliding Mode, LQR, LQG, MPC, H-∞, Fuzzy Logic Control, Neural Control, Discrete Control Systems
Model-Based Control System Development
Learning Outcomes:
Exposure to control algorithms for automotive applications.
Applications:
Control features development
Module Description:
Covers the software design principles for automotive ECUs,including layer separation,OS/RTOS integration, and fail-safe mechanisms. Provides insights into modular, scalable,and maintainable code structure..
Concepts Covered:
Major Software Modules, Libraries, Classes, Functions, Tasks, Processes, Threads, APIs, Data Models, Layers, and Their Responsibilities within a Software System
Tasks, Timing and Scheduling; Threads
Interaction between Modules
Component Diagrams, Sequence Diagrams, API Definitions
Evaluation for Maintainability, Testability, Reusability, Scalability, and Performance of the Software
Learning Outcomes:
Describe layered and modular ECU software architecture
Applications:
Design of scalable and modular software.
Module Description:
Equips learners with practical programming skills for realizing ECU functions, emphasizing embedded C, and hardware abstraction.
Concepts Covered:
Representation of Numbers, Fixed Point vs. Floating Point
Embedded C/C++
Assembly Programming
Device Drivers
Managing Input/Output Signals and Software Debouncing
Compose efficient embedded programs for automotive hardware
Applications:
ECU software development
Module Description:
Focuses on rigorous methods to verify and validate embedded software and hardware in automotive systems. Highlights test planning, and coverage analysis.
Concepts Covered:
Test Methodologies: Black Box, White Box, Grey Box, RPC, SIL, PIL, HIL, Unit Testing, Integration Testing, System Testing, Regression Testing
Test Case Design and Development
Code Coverage
Static Analysis
Formal Verification
Test Tools: CANoe, CANalyzer, Vehicle Spy, ETAS INCA, ATI Vision, Lauterbach TRACE32, Diagnostic Tools
Test Automation: Python, Excel, MATLAB, CAPL, Vector VT System, ETAS LABCAR, etc.
Diagnostic Protocols – UDS
Learning Outcomes:
Design comprehensive test plans for automotive ECUs.
Applications:
Product release readiness assessment
Module Description:
Teaches calibration techniques and release management processes, essential for fielding and maintaining automotive embedded systems. Covers calibration workflows, data management, and version control for production ECUs.
Concepts Covered:
Data Analysis Techniques, Statistical Analysis, and Optimization Techniques
Model-Based Calibration and AI in Calibration
Calibration Tools: ETAS INCA, Vector CANape, ATI Vision
