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Course Outline
Introduction to EV Software and Embedded Systems
- Overview of EV software architecture.
- Key components of embedded systems in EVs.
- Software challenges specific to electric mobility.
Control Systems for Electric Vehicles
- Designing software for motor control and power management.
- Battery management system (BMS) software integration.
- Monitoring and diagnostics for system health.
Real-Time Processing and Data Management
- Implementing real-time data acquisition and processing.
- Data fusion from multiple sensors.
- Ensuring low-latency control in dynamic environments.
Autonomous Driving and AI Integration
- Utilizing machine learning for perception and decision-making.
- Integrating AI algorithms with embedded systems.
- Testing and validation of autonomous functions.
Advanced Software Safety and Compliance
- Applying ISO 26262 for functional safety.
- Ensuring cybersecurity in connected EV systems.
- Developing fault-tolerant software architectures.
Hands-On Lab: Building a Basic EV Control System
- Setting up an embedded software environment.
- Programming control algorithms for motor operation.
- Testing and debugging the control system.
Best Practices for EV Software Development
- Maintaining code quality and efficiency.
- Collaborating within multidisciplinary engineering teams.
- Documenting and testing software components.
Future Trends and Innovations in EV Software
- Next-generation AI for fully autonomous electric vehicles.
- Integration with IoT and smart infrastructure.
- Predictive maintenance using AI-driven analytics.
Summary and Next Steps
Requirements
- Proficiency in embedded systems programming.
- Familiarity with automotive software development.
- Comprehension of real-time processing and control systems.
Target Audience
- Software developers specializing in EV applications.
- Embedded systems engineers within the automotive industry.
- AI experts focused on autonomous EV technology.
14 Hours