Design a Complete Battery Management System
End-to-end BMS design — cell-monitoring IC selection, balancing strategy, SoC/SoH algorithm, fault diagnostics. Defended in a panel review on the cohort's final week.
Certification Course in Advanced Driver Assistance Systems (ADAS) for EVs




Course Curriculum
Certification Course in Advanced Driver Assistance Systems (ADAS) for EVs
Topic 1: ADAS Prerequisites 1 - Vehicle Fundamentals & Electronics
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Topic 2: ADAS Prerequisites 2 - Embedded Systems, Programming & Control
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Topic 3: ADAS Prerequisites 3 - AI & ML, Safety Standards & Testing
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Topic 4: ADAS Overview - Sensors, Features & Automation
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Topic 1: Overview of Development Models, Testing, and Simulation
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Topic 2: LDW & LKA - Sensor Based Implementation & Validation in MATLAB
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Topic 3: MATLAB and Simulink Overview for ADAS Applications
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Topic 4: Line-by-Line Code Demonstration - MATLAB Simulation of ADAS Lane Keeping System
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Topic 5: Camera-Based Lane Detection and Video Processing for ADAS Using MATLAB
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Topic 6: Sensor Limitations and ADAS Functional Block Diagram
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Topic 7: Radar-Based Object Detection in ADAS - Principles, MATLAB Simulation, & Code Walkthrough
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Topic 1: MATLAB Syntax, Operations, Variables, Arrays, & Matrices
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Topic 2: Writing Scripts and Functions, including Loops & Control Structures
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Topic 3: Activity - Solve Linear Equations using MATLAB scripts & functions
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Topic 1: Importing, Exporting, & Pre-Processing Data in MATLAB
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Topic 2: Basic Statistical Analysis & Filtering Techniques
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Topic 3: Plotting & Visualizing Data, including 3D plots
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Topic 4: Activity - Analyze and Visualize ADAS Sensor Data
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Topic 1: Overview of Camera, RADAR, LIDAR, and Ultrasonic Sensors - Working Principles, Advantages, Limitations, & Data Types
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Topic 2: Sensor Data Formats & Pre Processing
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Topic 3: Introduction to Sensor Fusion
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Topic 4: Activity - Simulate and Visualize Sensor Data in MATLAB.
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Topic 1: Basics of Digital Signal Processing: Sampling, Fourier Transforms, & Filtering
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Topic 2: Noise Reduction & Feature Extraction from Sensor Data
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Topic 3: Activity - Implement Noise Reduction Filters on Sensor Data using MATLAB
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Topic 1: Algorithms for Lane Detection, Object Detection, and Tracking
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Topic 2: Implementing ADAS Algorithms in MATLAB using Image Processing & Computer Vision Techniques
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Topic 3: Activity - Develop a Lane Detection Algorithm using MATLAB
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Topic 1: Setting up ADAS Simulations in MATLAB - Environment & Scenario Setup
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Topic 2: Using MATLAB’s ADAS Toolbox for Simulation
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Topic 3: Evaluating Simulation Performance
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Topic 4: Activity - Run an ADAS Simulation Scenario & Evaluate Its Performance
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Topic 1: Review of Key Concepts & Introduction to a Comprehensive ADAS Project
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Topic 2: Project Planning, Task Division, and Timeline Creation
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Topic 3: Activity - Begin Project Planning & Role Assignments
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Project: Lane Departure Warning System ADAS Design and Simulation
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Congratulations on Successfully Completing the Course!
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Earn a certificate
Every completing learner receives a joint certificate signed by three accrediting bodies — ASDC (Government of India), AICTE NEAT, and IIT Jammu I3C. The certificate carries a public verification code recruiters can validate in seconds via emobility.careers.

System Requirements
Lightweight on hardware — most modern laptops handle it. Software is free / academic-licensed.
Associated Skills
DIY Projects Included
Every project is defended in front of an industry panel and graded by our mentors. Your portfolio after this course is what gets you the interview.
End-to-end BMS design — cell-monitoring IC selection, balancing strategy, SoC/SoH algorithm, fault diagnostics. Defended in a panel review on the cohort's final week.
Model a 2-wheeler / 4-wheeler powertrain from motor to wheels, run it through MIDC + WLTP drive cycles, and predict range against the spec sheet.
Assemble a Level-1 / Level-2 EVSE on a benchtop kit — CP/PP signalling, contactor + relay logic, Type-2 connector wiring, safety lockouts.
Course Benefits
Two audiences. Two angles. Pick the one that maps to where you are today.
What this course includes
Who can take this course?
Personalised Trainer Support

Every cohort is paired with a dedicated mentor — typically an IIT-trained EV practitioner with 6+ years of industry experience. Weekly 1:1 sessions, async doubt-clearing turnaround within 24 hours, project reviews before submission, and mock interviews tuned to the role you're targeting.
FAQ
Technical Expertise You Will Gain
Hardware competence is the differentiator. Each module pairs theory with a tooling-grounded exercise so your skill claims are demonstrably true at interview time.
Placement Network
Engineers from this course currently build at OEMs across India, Europe, North America and the Middle East.















The world's only EV-specific job network. 300+ hiring partners across India, Europe, North America and the Middle East.
Government-Certified · Industry-Endorsed · Learner-Approved

Automotive Skills Development Council — recognised by major OEMs worldwide.

National Educational Alliance for Technology — issued under AICTE's premium learning ecosystem.

Recognised by India's Ministry of Education + NSDC skilling framework — accepted globally.
Alumni Stories
Engineers from Mercedes-Benz, Cummins, Infineon, GM, Hydro One, John Deere and more.
The hardware-first approach made all the difference. Came in from a generalist electronics background; the BMS and motor-drive modules gave me concrete vocabulary I could carry into Infineon interviews.

Industry-grade depth without the academic ivory tower. The MATLAB Simulink + capstone defence prepared me for exactly the kind of cross-functional EV problems we solve at Cummins.

Started with electronics, walked into a BMS role in Germany. The applied projects + mentor reviews are what carried me through — the certifications opened the door, the depth got me hired.











