Battery Electric Vehicle Route Energy Simulation Executive Summary We propose a novel simulation framework that merges real-time mapping data with advanced vehicle modeling to accurately predict energy consumption for battery electric vehicles (BEVs). This solution addresses the critical market need for reliable range estimation—a key factor limiting widespread BEV adoption. By integrating Google Maps API with sophisticated BEV models, our approach provides unprecedented accuracy in trip planning and energy management for electric vehicle users. Problem Statement Range anxiety continues to be a significant barrier to electric vehicle adoption. Current energy estimation methods fail to account for the complex interplay between:
Route-specific elevation changes Real-time traffic conditions Driver behavior patterns Environmental factors Vehicle-specific performance characteristics
This results in unreliable range predictions, suboptimal route planning, and underutilized BEV capabilities. Our Solution Our proposed simulation framework creates a comprehensive end-to-end workflow that:
Leverages real-world data by integrating Google Maps API to capture actual route topology, traffic conditions, and elevation profiles Generates realistic driving cycles customized to specific routes rather than using generic test cycles Employs advanced BEV modeling with accurate powertrain dynamics, battery characteristics, and thermal effects Delivers actionable insights including energy consumption forecasts, optimal routing, and performance metrics
Technical Approach The solution integrates three core technical components:
- Dynamic Route Data Acquisition
Secure real-time route information through Google Maps API integration
Extract critical parameters including elevation, segment types, and traffic patterns
Transform geographic data into simulation-ready inputs
- Sophisticated Vehicle Modeling
Implement detailed BEV physics using MATLAB/Simulink
Model key subsystems including battery, motor, regenerative braking, and auxiliaries
Account for energy conversion efficiencies and thermal dependencies
- Intelligent Analysis & Visualization
Calculate energy consumption with segment-by-segment precision
Provide meaningful metrics for range estimation and trip planning
Deliver intuitive visualizations for technical and non-technical users
Development Status
Established API integration with Google Maps services Developed the core driving cycle generation algorithms Created functional BEV simulation models with thermal considerations Validated initial results against real-world driving data Implemented basic visualization and analysis tools
Next Steps
Refine machine learning components for personalized driver profiles Expand weather and environmental factor integration Develop charging infrastructure integration for long-route planning Create user-friendly interfaces for widespread accessibility Conduct extensive validation across diverse routes and vehicles
Conclusion Our Battery Electric Vehicle Route Energy Simulation project represents a significant advancement in addressing the practical challenges of electric vehicle adoption. By combining cutting-edge simulation techniques with real-world mapping data, we deliver a solution that transforms how drivers interact with and trust their electric vehicles. We respectfully request your consideration and support to bring this innovative solution to completion, ultimately accelerating the transition to sustainable transportation.
License : Whole Project is MIT licensed