Specialization Course in
Data Analytics and Cybersecurity in Electric Vehicles
Course Overview:
This course provides a comprehensive understanding of how data analytics and cybersecurity are transforming the electric vehicle (EV) ecosystem. Learners will explore data-driven insights for performance optimization, predictive maintenance, and fleet management, along with advanced cybersecurity frameworks that safeguard EV systems, networks, and communications. Hands-on practice with Python, big data frameworks, and cybersecurity simulations will equip learners with practical skills relevant to the evolving EV industry.
Course Objectives:
Explain the role of data analytics in EV performance monitoring, energy optimization, and predictive maintenance.
Analyze cybersecurity threats in EV ecosystems and apply standards and frameworks for risk mitigation.
Collect, preprocess, and analyze EV-generated data using big data and machine learning tools.
Develop predictive models and digital twin concepts for real-time monitoring and fleet optimization.
Apply security protocols and encryption techniques to safeguard EV communication and control systems
Evaluate advanced trends such as post-quantum cryptography, blockchain, and AI in EV applications.
Work on real-world case studies and capstone projects bridging analytics with cybersecurity in EVs.
Course Modules
Significance of data in EV performance & fleet management
Fault detection, energy optimization, big data applications (e.g., Tesla)
Introduction to EV cybersecurity: V2G, V2X, autonomous driving risks
Case study: EV industry challenges due to lack of analytics/security
Data sources: sensors, CAN, OBD-II, telematics
Data acquisition via IoT platforms, storage via Hadoop/Spark
Data cleaning, preprocessing, feature extraction
Hands-on: Python basics for EV data handling and visualization
Descriptive analytics: energy consumption, performance visualization
Predictive analytics: ML models for battery & motor health prediction
Digital twin applications for fleet and failure analysis
Hands-on: Scikit-learn predictive modeling mini-project
Cyber threats: physical, network, and software vulnerabilities
Standards: ISO/SAE 21434, UNECE WP.29
Securing BMS, controllers, telematics with encryption (TLS, AES)
Hands-on: Simulated attack and secure communication implementation
Data-driven optimization using AI (route planning, energy management)
Emerging cybersecurity trends: post-quantum cryptography, blockchain
Case studies: Tesla OTA updates, predictive fleet maintenance
Capstone project: Predictive maintenance or cybersecurity simulation
Target Industry Profiles
EV Data Analyst
Battery Data Scientist
EV Cybersecurity Engineer
Smart Mobility Data Specialist
Automotive Data Security Consultant
Fleet Analytics Manager
Connected Vehicle Security Analyst