Machine Learning and AI for Advanced Automotive Engineering Systems
Jointly organized by Rustamji Institute of Technology, BSF Academy, Gwalior and Electronics & ICT Academy, PDPM IIITDM Jabalpur.
About the Programme
This Faculty Development Programme is designed to provide a systematic understanding of Machine Learning and Artificial Intelligence for advanced automotive engineering applications. The programme connects fundamental concepts with application-oriented learning through automotive case studies, vehicle data analytics, engine and emission modelling, electric vehicle systems, diagnostics and intelligent mobility.
The FDP aims to equip faculty members, researchers and industry participants with the ability to apply AI-enabled methods for modelling, prediction, optimisation and decision support in modern automotive systems.
Who can attend: Faculty members, students, fresh graduates, researchers and industry personnel working in allied disciplines.
Course Contents
- Artificial intelligence, machine learning and data science in automotive engineering
- Modern BS-VI engine technology, combustion and fuel injection
- Emission-control systems and after-treatment devices
- Electronic engine management systems, sensors, actuators and ECUs
- Vehicle communication networks: CAN, LIN, FlexRay and automotive Ethernet
- Automotive data acquisition, preprocessing and feature engineering
- AI modelling of engine performance, fuel consumption and emissions
- Machine learning for EV battery SOC and SOH estimation
- Predictive maintenance, fault diagnosis and vehicle health monitoring
- Computer vision for ADAS and intelligent mobility
Hands-On Sessions
- Python/MATLAB-based automotive data-analysis workflows
- Preprocessing, feature selection and visualisation of automotive datasets
- Analysis of BS-VI engine performance and emission data
- Engine sensor-data interpretation and basic diagnostics
- CAN-bus data acquisition, decoding and analysis
- Regression and classification models for automotive applications
- Battery SOC/SOH prediction
- Battery thermal-management data analysis
- Computer-vision demonstrations for automotive perception
- Mini-project discussion and FDP outcome report
Key Details
Programme Coordinators
+91-9826824648
gauravsaxena@rjit.ac.in
Prof. Anand Baghel:
+91-9039583563
anandbaghel@rjit.ac.in
Resource Persons
Programme Features
- Expert lectures by academicians and automotive specialists
- Coverage of BS-VI engines, emission control and engine management systems
- Practical exposure to sensors, ECUs, diagnostics and vehicle networking
- Python/MATLAB training for automotive data analysis
- Case studies on EV batteries, fault diagnosis and ADAS
- Certificate on successful completion as per attendance and assessment norms
Expected Learning Outcomes
- Understand BS-VI engines and emission-control systems
- Explain engine-management systems, sensors, actuators and ECU operation
- Analyse CAN, LIN, FlexRay and automotive Ethernet data
- Process and interpret engine, vehicle and EV datasets
- Develop basic ML models for prediction, diagnosis and maintenance
- Apply AI techniques to battery, thermal and vehicle-health applications
Registration and Contact
Participants may register through the Google Form using the Register Now button above.
For queries: Dr. Gaurav Saxena / Prof. Anand Baghel, Department of Automobile Engineering, RJIT BSF Academy, Gwalior; Prof. Manu Shrivastava, PDPM IIITDM Jabalpur.