Opportunity brief
Overview
TechTrack Case Battle 3.0 is a real-world technical challenge designed to test participants' ability to understand complex engineering problems and develop practical, data-driven solutions.
This year, participants will work on an Electric Vehicle (EV) Range Prediction problem, using a supplied EV specification dataset to develop a complete regression-based machine learning solution for predicting vehicle range.
The challenge focuses not only on the final model but on the complete machine-learning workflow, including data cleaning, exploratory data analysis, feature engineering, model development, evaluation, reproducibility, documentation, and a working interactive demonstration.
The dataset consists of static EV specifications and does not contain trip-level telemetry such as traffic conditions, weather, HVAC usage, State of Charge (SoC), or State of Health (SoH). Therefore, the challenge specifically focuses on specification-based EV range prediction rather than real-time range estimation.
Eligibility
TechTrack Case Battle 3.0 is open to:
- Undergraduate and postgraduate students across India.
- Students from different colleges and academic branches.
- Cross-college and cross-branch teams.
- Participants who are members of only one team.
Competition Structure
Stage 1: Problem Statement Release
The detailed EV Range Prediction problem statement, along with the required dataset and challenge guidelines, will be released on 25 August 2026.
Participants will analyse the given dataset and develop a suitable machine-learning approach to solve the problem.
Round 1: Documentation & Solution Submission
Teams will develop their complete regression solution and submit their documentation within the prescribed deadline.
The submission should demonstrate:
- Data cleaning and preprocessing
- Exploratory data analysis
- Feature engineering
- Model development
- Model evaluation
- Reproducibility
- Technical documentation
- Working interactive demonstration
Submission Deadline: 4 September 2026
Submissions will be evaluated, and shortlisted teams will qualify for the final round.
Round 2: Offline Finals
The shortlisted teams will present and demonstrate their solutions before the jury in the offline final round at MANIT Bhopal.
Teams will be evaluated on their understanding of the problem, quality of data analysis, machine-learning approach, prediction performance, innovation, implementation, documentation, and ability to explain and defend their solution.
- Date: 9 September 2026
- Venue: MANIT Bhopal
General Rules & Guidelines
- Cross-college and cross-branch teams are permitted.
- A participant can be part of only one team.
- Teams cannot be modified after registration.
- All submitted work must be original.
- Participants must use the provided dataset and follow the challenge guidelines.
- Solutions must be reproducible and properly documented.
- Participants must adhere to the specified submission deadline.
- The organizers reserve the right to verify the authenticity and eligibility of submissions.
- The decision of the jury and organizers shall be final and binding.
Rewards & Opportunities
- Cash prizes worth ₹30,000
- Paid internship opportunities for the top three teams
- Hands-on experience with a real-world EV data science problem
- Opportunity to showcase solutions before an expert jury
- Exposure to machine-learning applications in the electric-vehicle domain
- Certificates for shortlisted finalists
Key Dates
- Problem Statement Release: 25 August 2026
- Round 1 — Documentation & Solution Submission: 4 September 2026
- Round 1 — Shortlist Announcement: 5 September 2026
- Round 2 — Offline Finals: 9 September 2026
Analyse. Predict. Innovate. Compete.