Engineering Practice Problem
Develop a navigation algorithm or app that relieves EV range anxiety. The solution should ingest geospatial data, charging station locations, battery consumption models and user preferences to compute routes that minimize travel time and ensure drivers always have access to chargers. Optional: integrate with existing mapping APIs and present the route on a user‑friendly interface.
Problem Statement
Despite improvements in battery technology, many EV drivers still experience range anxiety—fear that an electric vehicle may not have enough charge to reach its destination. Charging infrastructure often lags behind adoption; the UK had roughly 460 000 battery‑powered EVs but only 31 000 public charging points in 2022. Create a route‑planning tool that recommends optimal paths for EV drivers. It should consider vehicle range, battery state of charge, elevation, traffic, real‑time charging station availability and wait times. The tool must dynamically adjust routes if stations become unavailable and provide energy‑usage predictions.
Submission Guidelines
Use real or synthetic data about charging stations; explain assumptions (e.g. charging speeds, station availability).
Provide a clean, documented codebase (Python, JavaScript, etc.) with instructions to run your solution.
Show how your algorithm accounts for battery degradation, vehicle type and driving conditions.
Include readme with setup steps, dependencies and test scenarios.
Submit a brief report describing your algorithm, heuristics (e.g. A* search, dynamic programming) and performance metrics.
If you build a UI, describe the user journey and display screenshots or prototypes.Max File Size: 50 MB
Accepted Submission Types
Your PDF may include any combination of:
Functional prototype/app demonstrating route recommendations.
Code repository (GitHub link or ZIP) with implementation and documentation.
Technical documentation: algorithm description, pseudocode, diagrams.
Optional: demo video or interactive dashboard.
This practice problem is suitable for:
Judging Criteria
Algorithm design (25%) – Efficiency and correctness of the routing algorithm, including use of heuristics and data structures.
Data integration (15%) – Effective use of real or simulated charging station data and vehicle parameters.
Usability & interface (10%) – Clarity and intuitiveness of any provided UI or API; ability to customize preferences.
Adaptability & robustness (15%) – Handling of dynamic scenarios such as station unavailability, traffic changes, or battery degradation.
Documentation & code quality (15%) – Clarity of code, comments, readme file, and technical explanation.
Innovation (10%) – Incorporation of novel features (e.g., predictive wait times, energy consumption visualization).
Testing & results (10%) – Demonstrated examples and benchmarks showing improved outcomes or user benefit.
